System and method for fast context switching in GPU-based inference platforms

Abstract

A system and method reduce startup latency in a GPU-based inference platform by preparing standby model-serving execution environments before inference requests are received and restoring execution state in response to the requests without repeating a full initialization sequence. In some embodiments, snapshot data representing previously established execution state may be associated with the standby execution environments, and at least a portion of the snapshot data may be loaded, restored, or distributed in parallel across multiple GPUs or other accelerators to reduce model load time.

Description

PATENT Attorney Docket No. 2377.0005 (INFX-0001-WG) SYSTEM AND METHOD FOR FAST CONTEXT SWITCHING IN GPU-BASED INFERENCE PLATFORMS CLAIM TO PRIORITY

[0001] This patent application claims the benefit of U.S. Patent Application Ser. No. 63 / 774,181, filed March 19, 2025, and entitled “AN INFERENCE PLATFORM BASED ON INFERENCE INSTANCE SNAPSHOTS” (INFX-0001-P01). This patent application claims the benefit of Chinese Patent Application Ser. No. 202510396607.4, filed April 1, 2025, and entitled “AN INFERENCE PLATFORM BASED ON INFERENCE INSTANCE SNAPSHOTS” (INFX-0001-CN).

[0002] The content of the foregoing applications are hereby incorporated by reference in their entirety for all purposes.BACKGROUND

[0003] Computing systems and methods for high-performance inference services increasingly rely on graphics processing units (GPUs) to accelerate machine learning workloads. As models and data volumes expand, cloud and edge platforms are required to efficiently orchestrate resources to deliver low-latency, scalable inference while maintaining secure multi-tenant isolation. Existing methods and systems often experience significant startup delays due to lengthy initialization phases and slow data transfers to GPU memory, resulting in inefficient GPU utilization and degraded responsiveness. There remains a need for streamlined methods that accelerate GPU inference startup, optimize resource usage, and ensure secure operation in multi-tenant environments.SUMMARY

[0004] According to an aspect of the present disclosure, a method for reducing startup latency in a workload executed using at least one graphics processing unit (GPU) comprises preparing, before a request is received, a standby instance with a runtime environment and associating the standby instance with snapshot data including GPU state, restoring, after the request is received, the GPU state from the snapshot data into GPU memory and associating the GPU state with the standby instance to make the instance ready to execute, and executing the workload using the ready to execute instance.

[0005] According to some embodiments, preparing the standby instance may further comprise prepositioning the snapshot data in host memory.

[0006] According to some embodiments, restoring the GPU state may further comprise transferring the GPU state from host memory to GPU memory using parallel data loading across multiple GPUs.

[0007] According to some embodiments, the snapshot data may further comprise CPU state.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0008] According to some embodiments, the standby instance may be associated with a container runtime or virtual machine environment.

[0009] According to some embodiments, the method may further comprise restoring CPU state from the snapshot data into the runtime environment.

[0010] According to some embodiments, the standby instance may be maintained in a standby state without occupying GPU execution resources.

[0011] According to some embodiments, multiple standby instances may be created from a common snapshot source.

[0012] According to some embodiments, the snapshot data may include restoration metadata for associating CPU state and GPU state during restoration.

[0013] According to some embodiments, the method may further comprise pre-warming the snapshot data in accessible storage before the request is received.

[0014] According to some embodiments, the method may further comprise activating the standby instance by transitioning the standby instance to a ready state for inference serving.

[0015] According to some embodiments, the method may further comprise restoring GPU -resident state across multiple GPUs for a multi-GPU inference instance.

[0016] According to an aspect of the present disclosure, a computer-implemented method for reducing activation latency of a model-serving workload on accelerator hardware comprises initializing, on a host processor, an execution environment for a model-serving instance through at least a portion of a startup sequence that includes framework initialization and runtime configuration, capturing, after completion of the at least a portion of the startup sequence but prior to serving any external request, a snapshot that preserves execution state of the model-serving instance, the snapshot comprising CPU-resident state and GPU-related state data and including restoration metadata that identifies associations between the CPU-resident state and the GPU-related state data, storing the snapshot in storage accessible to a GPU node and pre-positioning at least a portion of the snapshot in host memory of the GPU node, maintaining a standby instance of the model-serving instance in the execution environment without allocating GPU memory for the model-serving instance and without loading GPU-resident state for the model-serving instance, receiving a request to execute the model-serving workload, selecting, in response to the request, the standby instance associated with the model-serving workload, coordinating, by a node-level controller, restoration of preserved execution state by restoring at least a portion of the CPU-resident state from the snapshot into the execution environment of the standby instance and loading the GPU-related state data from the snapshot into GPU memory of one or more GPUs of the GPU node and associating the loaded GPU-related state data with the standby instance in accordance with the restoration metadata, andPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) activating the standby instance as a ready instance and executing the model-serving workload using the ready instance on the one or more GPUs.

[0017] According to some embodiments, initializing the execution environment may comprise launching a container runtime or virtual machine environment and loading one or moremodel-serving framework libraries.

[0018] According to some embodiments, capturing the snapshot may be performed after model parameters have been loaded into memory but before any inference request is processed by the model-serving instance.

[0019] According to some embodiments, the snapshot may be generated at a plurality of different initialization checkpoints, and the method may further comprise selecting, for restoration, a snapshot corresponding to a desired readiness level.

[0020] According to some embodiments, the GPU-related state data may comprise one or more of model weights, executable kernels, tensor buffers, activation buffers, or KV cache structures.

[0021] According to some embodiments, pre-positioning the snapshot may comprise loading at least a portion of the snapshot into host memory while another portion of the snapshot remains stored in persistent storage.

[0022] According to some embodiments, maintaining the standby instance without allocating GPU memory may permit the GPU node to reassign GPU execution resources to a different workload while the standby instance is maintained.

[0023] According to some embodiments, selecting the standby instance may be based at least in part on predictive logic indicating an anticipated likelihood of receiving the request.

[0024] According to some embodiments, restoring the GPU-related state data may comprise transferring the GPU-related state data into GPU memory using parallel data transfers to multiple GPUs.

[0025] According to some embodiments, restoring the CPU-resident state and loading theGPU-related state data may be performed concurrently to reduce activation latency.

[0026] According to some embodiments, the node-level controller may manage GPU memory allocation across a plurality of GPUs before loading the GPU-related state data.

[0027] According to some embodiments, activating the standby instance may comprisere-associating restored CPU-resident state and GPU-related state using the restoration metadata.

[0028] According to some embodiments, executing the model-serving workload may comprise performing inference using a large language model to generate output tokens responsive to a prompt included in the request.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0029] According to some embodiments, multiple standby instances may be maintained from a common snapshot, and the method may further comprise independently activating respective standby instances in response to respective requests.

[0030] According to some embodiments, the method may reduce request-path startup latency relative to a cold initialization by avoiding repeated framework initialization and model loading after receipt of the request.

[0031] According to an aspect of the present disclosure, a system for reducing activation latency of a model-serving workload on accelerator hardware comprises a GPU node comprising a host processor, host memory, and one or more graphics processing units (GPUs) each having GPU memory, a snapshot store configured to store a snapshot captured after an execution environment for a model-serving instance has completed at least a portion of a startup sequence, the snapshot comprising CPU-resident state, GPU-related state data, and restoration metadata associating the CPU-resident state with the GPU-related state data, an execution environment instantiated on the host processor and configured to maintain a standby instance of the model-serving instance without allocating GPU memory and without loading GPU-resident state for the standby instance, a gateway configured to receive a request to execute the model-serving workload, and a node-level controller in communication with the GPU node and the snapshot store, the node-level controller being configured to pre-position, prior to receipt of the request, at least a portion of the snapshot in the host memory, select, in response to the request, the standby instance associated with the model-serving workload, restore at least a portion of the CPU-resident state from the snapshot into the execution environment of the standby instance, and load the GPU-related state data from the snapshot into the GPU memory of the one or more GPUs and associate the loaded GPU-related state data with the standby instance in accordance with the restoration metadata, wherein the execution environment is further configured to activate the standby instance as a ready instance and execute the model-serving workload on the one or more GPUs using the restored CPU-resident state and the loadedGPU-related state data.

[0032] According to some embodiments, the execution environment may comprise a container runtime configured to load one or more model-serving framework libraries.

[0033] According to some embodiments, the snapshot stored in the snapshot store may be captured after model parameters have been loaded into memory but before any inference request is processed by the model-serving instance.

[0034] According to some embodiments, the snapshot data may include GPU-related state data comprising one or more of model weights, executable kernels, tensor buffers, activation buffers, or KV cache structures.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0035] According to some embodiments, the standby instance may be maintained without allocating GPU memory, thereby permitting the GPU node to reassign GPU execution resources to a different workload while the standby instance is maintained.

[0036] According to some embodiments, the system may further comprise a scheduler configured to select the standby instance based at least in part on predictive logic indicating an anticipated likelihood of receiving the request.

[0037] According to some embodiments, the node-level controller may be configured to load the GPU-related state data into GPU memory using parallel data transfers to a plurality of GPUs.

[0038] According to some embodiments, the node-level controller may be configured to restore CPU-resident state and load GPU-related state data concurrently to reduce activation latency.

[0039] According to some embodiments, the node-level controller may be configured to manage allocation of GPU memory across a plurality of GPUs prior to loading the GPU-related state data.

[0040] According to some embodiments, activating the standby instance may comprisere-associating restored CPU-resident state and restored GPU-related state data using the restoration metadata.

[0041] According to some embodiments, execution of the model-serving workload may comprise performing inference using a large language model to generate output tokens responsive to a prompt included in the request.

[0042] According to an aspect of the present disclosure, a system for reducing startup latency in a workload executed using at least one graphics processing unit (GPU) comprises a snapshot store storing snapshot data including GPU state, a gateway configured to receive a request for the workload, a scheduler configured to coordinate startup of a standby instance for the workload, a node agent configured to, before the request is received, prepare the standby instance with a runtime environment and associate the standby instance with the snapshot data without loading the GPU state into GPU memory and, after the request is received, restore the GPU state from the snapshot data into GPU memory and associate the restored GPU state with the standby instance to make the standby instance ready to execute the workload, and at least one GPU having the GPU memory.

[0043] According to some embodiments, the snapshot data may further comprise CPU-resident state and restoration metadata usable to re-associate CPU-resident state with the restored GPU state after the request is received.

[0044] According to some embodiments, the node agent may be further configured to pre-position at least a portion of the snapshot data in host memory prior to receipt of the request to reduce GPU state restoration latency.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0045] According to some embodiments, the standby instance may be maintained without allocating GPU execution resources, thereby permitting the at least one GPU to be reassigned to a different workload prior to receipt of the request.

[0046] According to some embodiments, the node agent may be configured to restore GPU state into GPU memory of a plurality of GPUs to support execution of the workload as a multi-GPU inference instance.

[0047] According to some embodiments, the scheduler may be further configured to initiate preparation of the standby instance based on predictive logic indicating an anticipated likelihood of receiving the request for the workload.

[0048] According to an aspect of the present disclosure, an apparatus for reducing startup latency in a workload executed using at least one graphics processing unit (GPU) comprises a processor configured to prepare, before a request is received, a standby instance with a runtime environment and associate the standby instance with snapshot data including GPU state, without loading the GPU state into GPU memory, and at least one GPU configured to receive, after the request is received, the GPU state restored from the snapshot data into GPU memory and associate the GPU state with the standby instance to make the instance ready to execute, wherein the processor is further configured to execute the workload using the ready to execute instance.

[0049] According to an aspect of the present disclosure, a method for providing tenant isolation in a multi-tenant, multi-accelerator computing environment comprises executing, in a guest user space, by a workload process, a workload associated with a tenant and issuing, by the workload process, a communication request associated with use of one or more accelerators of the node, intercepting, in the guest user space, the communication request, forwarding, through a guest kernel space to a host user space, the communication request for evaluation, evaluating, in the host user space, the communication request based at least in part on an isolation policy to determine whether the communication request is authorized, responsive to determining that the communication request is authorized, executing a communication operation using communication resources of the computing node, providing, in a host kernel space, kernel-level support for the communication operation, and isolating inter-accelerator communication usage and network communication usage across tenants while permitting execution of a distributed workload.

[0050] According to some embodiments, the workload may comprise a model-serving workload.

[0051] According to some embodiments, intercepting the communication request may comprise intercepting a communication-library call.

[0052] According to some embodiments, intercepting the communication-library call may be performed by a communication-call intercept library in the guest user space.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0053] According to some embodiments, forwarding the communication request may be performed by a container runtime in the guest kernel space.

[0054] According to some embodiments, evaluating the communication request may be performed in the host user space by a call security check.

[0055] According to some embodiments, evaluating the communication request may comprise determining whether the communication request corresponds to an allowed tenant context.

[0056] According to some embodiments, evaluating the communication request may comprise determining whether the communication request corresponds to an allowed accelerator allocation.

[0057] According to some embodiments, evaluating the communication request may comprise determining whether the communication request corresponds to an allowed inter-accelerator path.

[0058] According to some embodiments, evaluating the communication request may comprise determining whether the communication request corresponds to an allowed remote communication path.

[0059] According to some embodiments, executing the communication operation may be performed by a communication library in the host user space.

[0060] According to some embodiments, providing the kernel-level support may comprise providing, by a host kernel, access to host-level resources used to carry out the communication operation.

[0061] According to some embodiments, the inter- accelerator communication usage may comprise NVLink communication usage.

[0062] According to an aspect of the present disclosure, an apparatus for providing tenant isolation in a multi-tenant, multi-accelerator computing environment comprises one or more processors, one or more accelerators, and a memory storing instructions that, when executed by the one or more processors, cause the apparatus to implement a guest user space, a guest kernel space, a host user space, and a host kernel space, a workload process in the guest user space, the workload process configured to execute a workload associated with a tenant and to issue a communication request associated with use of the one or more accelerators, a communication control path extending from the guest user space through the guest kernel space to the host user space, the communication control path configured to intercept the communication request, forward the communication request for evaluation, and, responsive to authorization of the communication request, enable execution of a communication operation using communication resources of the apparatus, a policy enforcement component in the host user space, the policy enforcement component configured to evaluate the communication request based at least in part on an isolation policy, and a kernel component in the host kernel space, the kernel component configured to provide kernel-level support for thePATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) communication operation, wherein the apparatus is configured to isolate inter-accelerator communication usage and network communication usage across tenants while permitting execution of a distributed workload.

[0063] According to some embodiments, the workload may comprise a model-serving workload.

[0064] According to some embodiments, the communication request may comprise a communication-library call.

[0065] According to some embodiments, the communication control path may comprise a communication-call intercept library in the guest user space configured to intercept the communication request.

[0066] According to some embodiments, the communication control path may comprise a container runtime in the guest kernel space configured to forward the communication request for evaluation.

[0067] According to some embodiments, the policy enforcement component may comprise a call security check in the host user space configured to evaluate the communication request.

[0068] According to an aspect of the present disclosure, a method for reducing model load time in a multi-accelerator computing environment comprises loading, by a computing node, a model snapshot into a memory of a first accelerator of a plurality of accelerators, identifying, by the computing node, one or more additional accelerators of the plurality of accelerators for execution of a workload associated with the model snapshot, copying, by the computing node, at least a portion of the model snapshot from the first accelerator to the one or more additional accelerators over an inter-accelerator communication path, and initializing, by the computing node, the workload on the plurality of accelerators based at least in part on the copied at least a portion of the model snapshot.

[0069] According to some embodiments, copying the at least a portion of the model snapshot may comprise copying the at least a portion of the model snapshot from the first accelerator to the one or more additional accelerators in parallel.

[0070] According to some embodiments, copying the at least a portion of the model snapshot may comprise copying the at least a portion of the model snapshot according to a hierarchical distribution pattern across the plurality of accelerators.

[0071] According to some embodiments, the at least a portion of the model snapshot may comprise model weights.

[0072] According to some embodiments, the inter-accelerator communication path may comprise NVLink.

[0073] These and other systems, methods, objects, features, and advantages of the present disclosure will be apparent to those skilled in the art from the following detailed description of the preferred embodiment and the drawings.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0074] All documents mentioned herein are hereby incorporated in their entirety by reference.References to items in the singular should be understood to include items in the plural, and vice versa, unless explicitly stated otherwise or clear from the text. Grammatical conjunctions are intended to express any and all disjunctive and conjunctive combinations of conjoined clauses, sentences, words, and the like, unless otherwise stated or clear from the context.BRIEF DESCRIPTION OF THE FIGURES

[0075] The disclosure and the following detailed description of certain embodiments thereof may be understood by reference to the following figures:

[0076] Fig. 1 is a schematic diagram of a GPU inference system according to certain embodiments of the present disclosure.

[0077] Fig. 2 is a schematic of example processes for performing fast context switching using a staged startup arrangement.

[0078] Fig. 3 is a schematic of example operations that may be part of the first phase.

[0079] Fig. 4 is a schematic of example operations that may be part of the second phase.

[0080] Fig. 5 is a schematic of an example system architecture.

[0081] Fig. 6 is a flowchart of an example method for reducing startup.

[0082] Fig. 7 is a flowchart of an example method for reducing startup.

[0083] Fig. 8 is a schematic of one example software architecture of a node for providing tenant isolation.

[0084] Fig. 9 is a flowchart of an example method for providing tenant isolation.

[0085] Fig. 10 is a flowchart of an example method for reducing model load time in a multiaccelerator computing environment.DETAILED DESCRIPTION

[0086] In GPU-accelerated environments, a model-serving system commonly transitions an inference instance among multiple operational contexts. In one example, context may include a dormant context, in which compute resources are not actively assigned, a standby context in which certain runtime components are partially prepared, and an active context in which the instance is ready to execute inference requests. Movement between these contexts generally involves a context switch. As used herein, a context switch may include any transition in which the execution state associated with an inference instance is suspended, discarded, reassigned, or reestablished across one or more processing resources, including host processors, memory subsystems, and one or more GPUs. Although such transitions can improve resource utilization by allowing hardware to bePATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) reassigned when demand is low, the transitions themselves can introduce latency before an instance becomes available to serve a request.

[0087] In some deployments, inference demand is bursty, intermittent, or otherwise difficult to predict. A serving platform may therefore seek to conserve accelerator resources during periods of low utilization by moving one or more inference instances out of a fully active execution context. However, when new demand arrives, the system may be required to reverse that transition and restore sufficient execution state to resume or initiate inference. Even where the underlying model and runtime artifacts remain logically associated with the instance, the instance may nonetheless be unavailable for useful work until the relevant host-side and device-side state has been reconstructed, rebound, or reloaded. As a result, a request directed to an instance that is nominally provisioned, but not fully active, may still experience a startup delay comparable to a cold or semi-cold launch.

[0088] In many conventional systems, the latency associated with a context switch is not limited to a scheduler operation. Instead, the system may need to reconstruct a substantial amount of execution state before useful work can resume. In GPU-based deployments, delay can arise because model weights, intermediate buffers, executable kernels, and other GPU-resident state may need to be transferred or reconstructed in device memory before the inference instance is functionally ready. These operations can occur across multiple software and hardware layers, and the cumulative delay can be significant.

[0089] The effect of this latency may be amplified where an inference service is designed to satisfy interactive or near-real-time response requirements. In such settings, delays attributable to contextswitch overhead can materially affect quality of service, tail latency, and system throughput. For example, when a request is routed to an inference instance that has not yet restored its executable state, the request may be queued while initialization proceeds, or it may be redirected to another instance, thereby increasing routing complexity and potentially overloading already active resources. In multi-tenant systems, repeated reactivation of dormant instances can further interfere with scheduling fairness and resource balancing because multiple tenants may compete simultaneously for GPU memory, host bandwidth, and accelerator execution time.

[0090] The costs (e.g., time, hardware) associated with context switching is often more pronounced in large-model inference systems. In cases where multiple GPUs are involved, the system may need to restore or reestablish multi-GPU execution state and communication configuration before inference can begin. As model size increases, and as the amount of GPU-resident state (i.e., the number of model weights and other data stored in the GPU memory) grows, the time required to perform these context-switch-related operations can be a dominant contributor to request latency.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0091] The context switching delay is especially problematic in on-demand inference platforms in which instances are not kept fully active at all times. Maintaining a large number of fully initialized GPU-backed instances can consume substantial GPU memory and compute resources during idle periods, reducing overall utilization and increasing operating costs. However, releasing those resources and later reacquiring them typically requires another context switch, along with restoration of CPU and GPU execution state, thereby reintroducing startup delay. Conventional systems are therefore often forced into an undesirable tradeoff between responsiveness and efficiency: either reserve expensive accelerator resources to avoid repeated transitions, or release those resources and accept longer time-to-readiness when a new request arrives.

[0092] Accordingly, there is a need for systems and techniques that reduce the latency associated with transitioning an inference instance from a non-serving or partially prepared context into a ready-to-serve context. More particularly, there is a need for mechanisms that reduce the amount of state reconstruction performed during a context switch, while still permitting GPU and CPU resources to remain unoccupied until demand arises. Improvements in this area can decrease cold-start latency, improve elasticity, and enable more efficient sharing of accelerator resources.

[0093] As used herein, reference to a GPU, which may also be referred to herein as an accelerator, may refer broadly to any processing resource configured to perform at least a portion of an inference workload, model execution workload, tensor-processing workload, or other computationally intensive workload associated with a machine-learning model. Accordingly, a GPU is not limited to a graphics processing unit in a narrow architectural sense and may include, for example, a neural-processing unit, tensor-processing unit, artificial-intelligence accelerator, vector processor, digital signal processor, application-specific integrated circuit, field-programmable gate array, reconfigurable logic resource, another accelerator device, or any other hardware resource capable of performing model-related computation. In some embodiments, a GPU or accelerator may additionally or alternatively refer to a logical, virtualized, partitioned, emulated, abstracted, or software-defined processing resource that provides accelerator-type functionality, whether implemented using dedicated hardware, shared hardware, software, firmware, or a combination thereof.

[0094] As used herein, unless the context indicates otherwise, reference to a CPU may refer broadly to any host-side, companion, supervisory, orchestration, or control processing resource configured to manage, schedule, support, coordinate, initiate, monitor, or otherwise facilitate execution of an inference workload or related accelerator workload. Accordingly, a CPU is not limited to a conventional central processing unit in a narrow architectural sense and may include, for example, a general-purpose processor, host processor, control processor, management processor, servicePATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) processor, supervisory core, orchestration engine, virtual processor, logical processor, emulated processor, firmware-executed control resource, software-defined control resource, or another processing resource that performs control-plane, coordination, or support functions for an accelerator-side workload. In some embodiments, the processing resource referred to as a CPU may itself be implemented using accelerator-class hardware, including GPU-class hardware. For example, a system may include a first accelerator having comparatively greater computational capability, memory capacity, bandwidth, feature support, or cost, and a second accelerator having comparatively lesser computational capability, memory capacity, bandwidth, feature support, or cost, where the first accelerator performs host-side, supervisory, or coordination functions for workloads executed by the second accelerator. Thus, in some embodiments, an expensive or higher-capability GPU or other accelerator may function as a CPU with respect to a cheaper or lower-capability GPU or other accelerator.

[0095] In some embodiments, the disclosed distinction between a CPU and a GPU or accelerator may therefore be functional rather than limited to any particular instruction set, hardware vendor, chip architecture, packaging arrangement, or historical processor category. For example, a GPU or accelerator may correspond to a processing resource used primarily for execution of inference computations, while a CPU may correspond to a processing resource used primarily for orchestration, memory management, communication handling, scheduling, input processing, container management, security checking, or other support operations. In some embodiments, such processing resources may be implemented on separate chips, on a same chip, on a same package, in a system-on-chip architecture, as different partitions of a shared processor, or as distributed or virtualized resources. In some embodiments, both the resource referred to as a CPU and the resource referred to as a GPU may each be implemented using accelerator hardware, with the distinction based on relative function within the system rather than processor type.

[0096] The present disclosure describes systems, methods, and apparatuses to perform fast context switching in a GPU-accelerated inference environment. In some embodiments, an inference instance is transitioned among multiple operational contexts, including a non-serving context, a standby context, and a ready-to-serve context, without repeating a full initialization sequence each time a request is received. Rather than reconstructing execution state from scratch during each transition, the disclosed systems can preserve, cache, and restore execution state associated with the inference instance, including CPU state, GPU state, runtime metadata, memory state, and model-related data. By reducing the amount of state reconstruction performed during a transition, the disclosed systems can decrease the time required to switch an inference instance from an inactive or partially prepared state into an active serving state.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0097] In some embodiments, fast context switching is achieved by preparing a standby inference instance before request arrival and completing restoration of the remaining execution state in response to the request. For example, a container runtime, virtual machine environment, or other execution environment may be partially established in advance, while GPU memory remains unoccupied until needed. Upon receipt of an inference request, GPU-resident state and corresponding CPU-resident state may be restored from cached or stored snapshot data so that the inference instance can begin serving with reduced delay. This approach can reduce cold-start latency, improve responsiveness under elastic demand, and permit accelerator resources to remain available for reassignment when no request is pending.

[0098] An example embodiment allows for fast context switching using a two-phase startup process. In some embodiments, a first phase is performed before an inference request is received and includes preparing an inference instance to a standby state without occupying GPU execution resources. The first phase may include establishing at least a portion of a runtime environment, loading or associating runtime metadata, and preparing memory or other execution structures such that the inference instance is partially initialized and available for rapid activation. A second phase is performed in response to an inference request and includes restoring the remaining execution state needed to place the inference instance into a ready-to-serve state. For example, the second phase may include restoring GPU-resident state and corresponding CPU-resident state from snapshot data so that the inference instance can begin processing the inference request without repeating a full initialization sequence. By dividing startup into a preparatory phase and a request-driven restoration phase, the disclosed system can reduce latency associated with context switching while permitting GPU resources to remain unoccupied until demand arises.

[0099] The description herein references inference systems as a non-limiting examples for clarity of explanation. As used herein, an inference system may include any computing system configured to execute a previously trained machine-learning, deep-learning, statistical, or other predictive model to generate an output based on an input. By way of example, an inference system may receive an input such as text, image data, audio data, video data, telemetry, sensor data, structured records, or combinations thereof, and may execute one or more model operations to produce a corresponding result, such as a classification, prediction, ranking, recommendation, generated content , control signal, or other response or output. In some embodiments, the inference system may include a generative model, such as a generative large language model, configured to generate output text, tokens, sequences, or other content based on a language prompt, textual context, or other languagebased input. For example, the generative large language model may receive a prompt including a question, instruction, dialogue context, document content, code content, or other languagePATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) representation and may generate a corresponding textual response, completion, summary, translation, code output, or other generated content. In many implementations, an inference system may include one or more host processors, one or more accelerators such as graphics processing units, a runtime environment, memory resources, networking resources, and software components configured to load model state, maintain execution state, and serve requests in an on-demand manner.

[0100] Although the disclosed embodiments are described in the context of inference systems, the described techniques are not limited to model-serving workloads. More generally, the disclosed embodiments may be applied to other systems in which an execution environment transitions among multiple operational contexts and in which restoration, transfer, or reestablishment of execution state contributes materially to startup latency. For example, similar techniques may be applied in data-processing systems, batch-computing systems, stream-processing systems, simulation environments, rendering systems, media-processing pipelines, database or cache services, virtual desktop or remote application environments, scientific-computing platforms, distributed training or fine-tuning systems, and other accelerator-backed or memory-intensive computing systems. In each such case, a system may benefit from reducing the amount of state reconstruction performed when transitioning an execution instance from an inactive or partially prepared state to an active state. Accordingly, references to inference systems should be understood as illustrative rather than limiting, and embodiments herein may be implemented in any environment having analogous context-switching, state-restoration, resource-allocation, or startup-latency challenges.

[0101] As used herein, a workload may refer to a unit of processing to be performed by an inference system. In some embodiments, a workload may be represented by, associated with, or derived from one or more requests received by the inference system. For example, a workload may correspond to a single inference request, a batch of inference requests, a sequence of related inference requests, a streaming session, a prompt-processing task, a token-generation task, or another set of operations to be executed using one or more models.

[0102] In some embodiments, where the inference system is configured to serve language-based models, a workload may include a prompt or may be generated based on a prompt. For example, a workload may include processing a prompt, generating one or more output tokens responsive to the prompt, generating a completion, response, summary, translation, or other language output, or performing one or more intermediate operations associated with prompt execution. A prompt may include text, dialogue context, system instractions, retrieved content, code content, structured input, multimodal input, or combinations thereof. Accordingly, a workload may correspond not only to an externally received request itself, but also to machine operations triggered by that request, includingPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) prompt ingestion, context construction, model-state restoration, token generation, post-processing, and response delivery.

[0103] In some embodiments, a workload may therefore refer broadly to computational work associated with serving one or more requests, prompts, or model-execution tasks. A workload may be characterized by one or more attributes such as model identity, prompt length, input type, batch size, expected output length, latency requirement, priority, tenant association, accelerator requirement, memory requirement, or duration. The term workload may encompass both a userfacing request and the underlying execution activity performed by the computing system to generate a corresponding output.

[0104] Referencing Fig. 1, an example inference system 100 includes one or more GPU nodes 102 configured to access one or more models 104, receive an inference request 106, and provide an inference response 108. In some embodiments, the GPU node 102 may include one or more processors, one or more graphics processing units, memory resources, and runtime components configured to load, restore, or execute a model for inference processing.

[0105] As used herein, a GPU node, which may also be referred to as a GPU server, may include a specialized computing server configured to perform high-throughput and accelerator-backed computing tasks such as machine-learning training, model inference, simulation, data analytics, or other parallel-processing workloads. In some embodiments, a GPU node may operate as a worker node within a larger cluster or distributed computing environment and may include a combination of host-side compute resources, accelerator resources, memory resources, storage resources, and communication resources that cooperate to execute assigned workloads. The GPU node may be configured to receive work assignments from a scheduler, receive requests or data from one or more external systems, execute one or more workload stages using host processors and accelerators, and return one or more results to another component of the distributed system.

[0106] Referring to Fig. 1, an example GPU node 102 is illustrated as part of an inference-serving system. In some embodiments, the GPU node 102 may include a specialized computing server configured to perform high-throughput and accelerator-backed computing tasks such as machinelearning training, model inference, simulation, data analytics, or other parallel-processing workloads. As shown in FIG. 1, the GPU node 102 may receive an inference request 106, access one or more models 104, execute one or more inference operations using host-side and device-side resources, and provide an inference response 108. In some embodiments, the GPU node 102 may operate as a worker node within a larger cluster or distributed computing environment and may include a combination of compute resources, accelerator resources, memory resources, storage resources, and communication resources that cooperate to execute assigned workloads.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0107] In some embodiments, the GPU node 102 may include a plurality of graphics processing units, such as a first GPU 118, a second GPU 120, and a third GPU 122, that serve as accelerator devices for computationally intensive operations. The GPUs 118, 120, and 122 may be configured to perform tensor-processing operations, matrix operations, attention operations, token-generation operations, image-processing operations, simulation operations, or other highly parallel computations. Although FIG. 1 depicts three GPUs 118, 120, and 122 for purposes of illustration, the GPU node 102 may include two to eight or more high-performance GPUs, or another suitable number of GPUs, in various embodiments. By way of non-limiting example, the GPUs may include devices provided by NVIDIA, such as H100 or A100 GPUs, where NVIDIA devices are one example only, or may include accelerators provided by another vendor, such as AMD Instinct accelerators. In some embodiments, the GPUs 118, 120, and 122 may each include device-local memory and may be used independently or cooperatively to execute one or more portions of a workload.

[0108] In some embodiments, the GPU node 102 may further include one or more central processing units, such as a CPU 112, configured to manage host-side operations associated with the workload. For example, the CPU 112 may perform data preprocessing, request handling, orchestration, control-flow execution, container management, runtime management, scheduling support, memory management, networking operations, storage operations, and task dispatch to the GPUs 118, 120, and 122. By way of non-limiting example, the CPU 112 may include one or more multi-core server processors such as Intel Xeon processors or AMD EPYC processors. In some embodiments, the CPU 112 may cooperate with the GPUs 118, 120, and 122 such that host-side logic prepares and feeds data or execution state to the GPUs while the GPUs perform at least part of the accelerator-suited computation.

[0109] In some embodiments, the GPU node 102 may include a system memory 110 accessible to the CPU 112. For example, the system memory 110 may include DDR4 memory, DDR5 memory, or another form of system random-access memory used to buffer input data, maintain operating-system state, store runtime metadata, hold model artifacts, store snapshot data, and support execution of host-side software components. In some embodiments, the capacity of the system memory 110 may be selected based on the number of GPUs, the size of the models 104 to be served, the expected batch size, the size of one or more prompts or context windows, the number of tenants, or other workload characteristics. For example, the GPU node 102 may include hundreds of gigabytes of system memory, and in some implementations may include approximately 256 gigabytes or more of system memory, or approximately 128 gigabytes or more of system memory per GPU, although other memory capacities may be used.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0110] In some embodiments, the GPU node 102 may further include one or more interconnects, such as interconnect 114 and interconnect 116, that permit communication among host-side and device-side components. For example, the interconnects 114 and 116 may include one or more host-to-device communication paths, one or more device-to-device communication paths, or both. In some embodiments, the GPUs 118, 120, and 122 may communicate with one another using a GPU-to-GPU interconnect such as NVEink or NVS witch, where NVIDIA-based interconnect technologies are one non-limiting example. In other embodiments, another peer-to-peer or switched accelerator interconnect may be used. Such interconnects may permit direct or relatively direct exchange of tensors, model state, activation data, cache data, synchronization information, or other execution data among GPUs at bandwidths greater than those available through standard host-mediated communication paths. In some embodiments, the GPU node 102 may also include one or more PCIe buses or PCIe switches, such as PCIe Gen4 or PCIe Gen5 components, that couple the CPU 112, the system memory 110, the GPUs 118, 120, and 122, and one or more peripheral devices. The interconnect fabric represented by interconnect 114 and interconnect 116 may therefore provide a communication backbone for host-to-device transfers, device-to-host transfers, storage access, and peripheral communication.

[0111] In some embodiments, the GPU node 102 may include one or more networking interfaces configured to support communication with other nodes, storage systems, gateways, schedulers, or client-facing services, although such networking interfaces are not expressly shown in FIG. 1. For example, the GPU node 102 may include one or more high-speed network interface cards. In some embodiments, the networking interfaces may support remote direct memory access to permit lower-latency and lower-overhead data movement across nodes in a distributed deployment. In some embodiments, such networking interfaces may be beneficial where model state, prompt data, inference requests, or snapshot data is exchanged among multiple nodes or external services.

[0112] In some embodiments, the GPU node 102 may further include one or more local storage devices configured to provide high-performance persistent storage, although such storage devices are not expressly shown in FIG. 1. For example, the GPU node 102 may include one or more nonvolatile memory express solid-state drives used to store operating-system data, executable binaries, container images, model files, checkpoints, snapshots, logs, temporary files, or cached datasets. The local storage may be used to reduce latency associated with repeatedly retrieving large artifacts from remote storage and may be used to cache model state or snapshot data for faster restoration. In some embodiments, the GPU node 102 may include multiple NVMe storage devices, such as two or more multi -terabyte solid-state drives, although other storage counts and capacities may be used.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0113] Accordingly, FIG. 1 illustrates that the GPU node 102 may include a coordinated set of structural components including the CPU 112, the system memory 110, the interconnects 114 and 116, and the GPUs 118, 120, and 122, and may further include networking interfaces, storage devices, and supporting cooling and power systems in various embodiments. By way of non-limiting example, an artificial-intelligence training or inference node may include two CPUs, eight GPUs coupled using NVLink, one terabyte or more of system memory, multiple high-speed network interfaces, and multiple NVMe solid-state drives. However, the disclosed subject matter is not limited to that configuration, and the GPU node 102 may include fewer or more components, different interconnect technologies, different memory capacities, different storage capacities, or different processor and accelerator types in various embodiments.

[0114] Referencing Fig. 1, models 104 may represent one or more machine-learning models, deeplearning models, large language models, or other executable model artifacts that are available to the GPU node 102 for inference operations. In some embodiments, the models 104 may be stored locally at the GPU node 102, in storage accessible to the GPU node 102, or in another repository from which model state or snapshot data may be obtained. The GPU node 102 may load, cache, restore, or otherwise access the models 104 in connection with servicing inference workloads.

[0115] The inference request 106 may include an input to be processed by the GPU node 102 using at least one of the models 104. By way of example, the inference request 106 may include text, image data, audio data, video data, structured data, sensor data, or other input data for which a model output is to be generated. Upon receiving the inference request 106, the GPU node 102 may activate, restore, or execute an inference instance associated with a selected model and process the input in accordance with the selected model. The inference response 108 may include an output generated by the GPU node 102 based on the inference request 106. The output may include, for example, generated text, a classification, a prediction, a recommendation, a score, transformed content, or another result produced using the selected model.

[0116] In some embodiments, the inference request 106 may correspond to any of a plurality of different inference tasks, and the GPU node 102 may determine, based on the content of the inference request 106, that processing of the inference request 106 is to be performed using a selected one of the models 104. For example, different models of 104 may be associated with different tenants, different applications, different model sizes, different languages, different domains, different performance profiles, or different inference functions. Accordingly, at a given time, the GPU node 102 may have access to multiple models 104 even though fewer than all of the models 104 are fully active in GPU memory.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0117] In some embodiments, only a subset of the models 104 may be active at a particular time due to GPU memory constraints, scheduling constraints, power constraints, or efficiency considerations. For example, a first model may be active and ready to serve a first class of inference requests, while a second model, although available to the system 100, may not currently occupy GPU memory. In some instances, the second model may be maintained in a non-serving context or a standby context in which at least some associated execution state is not fully loaded into GPU memory. In other instances, the second model may be represented by snapshot data, cached state, metadata, or model files accessible to the GPU node 102 but not yet restored into an active inference instance.

[0118] As a result, when the inference request 106 arrives, the GPU node 102 may determine that the inference request 106 is associated with a model that is not currently active in GPU memory. In a conventional system, servicing such a request may require a context switch in which the GPU node 102 initializes or resumes an execution environment for the selected model, allocates GPU memory, loads model-related state into GPU memory, restores CPU-resident state, and establishes runtime structures needed to begin inference execution. These operations may introduce delay before the inference response 108 can be generated, particularly where the selected model is large, where multiple models compete for limited accelerator resources, or where the model-specific execution state includes substantial CPU-resident and GPU-resident data.

[0119] In some embodiments, the context switch may take a comparatively long time because the transition is not merely a logical reassignment of a request from one execution context to another, but instead may require reconstruction of a substantial amount of execution state across multiple layers of the system 100. For example, when the inference request 106 is directed to a model 104 that is not currently active in GPU memory, the GPU node 102 may need to start or resume a container or virtual machine runtime, restore process state, reload or rebind runtime metadata, reestablish networking or communication objects, map host-memory regions, and reestablish pinned hostmemory registrations used for GPU access. In addition, the GPU node 102 may need to allocate or assign GPU memory, restore model weights, buffers, executable kernels, and other GPU-resident state, and make such state available to the execution environment before inference can begin. These operations may involve coordination among the host processor, memory subsystem, container runtime, virtual machine components, GPU driver stack, and one or more GPUs, such that the overall delay reflects the cumulative cost of many dependent operations rather than a single switch event.

[0120] The latency may further increase where the amount of state to be restored is large. For example, modern inference workloads may involve substantial CPU-resident state and substantialPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) GPU-resident state, including model parameters, activation-related buffers, key-value cache structures, compiled GPU binaries, and associated metadata. Transferring such data into GPU memory may itself consume a material amount of time because the restore path may be constrained by available host-to-device bandwidth, such as PCIe bandwidth, storage throughput, or network throughput where snapshot data is stored remotely. Even where storage access is fast, the GPU node 102 may still be required to move a large quantity of data from host memory or storage into GPU memory before the selected model becomes executable. In multi-GPU implementations, additional time may be consumed to restore device state across multiple GPUs and to reestablish multi-GPU communication configuration before the inference instance is ready.

[0121] In some embodiments, a normal context switch may take on the order of, several seconds, tens of seconds, or even minutes, depending on the amount of execution state to be restored. Such delays are unacceptable for many applications because the request cannot be meaningfully serviced until the execution context has been fully restored and the model is ready to generate output. In low-latency, on-demand inference systems, service targets may call for startup and response behavior within only a few seconds, such as less than about 5 seconds, whereas conventional context-switch delays can exceed that target by a wide margin.

[0122] Embodiments of the present disclosure provide for systems, apparatuses, and methods for improved context switching. In some embodiments, the disclosed techniques reduce the amount of work performed after an inference request is received by shifting selected preparation operations to an earlier time and by preserving execution state in a form that can be restored without repeating a full initialization sequence. For example, an inference instance may be prepared to a standby context before request arrival, while remaining GPU state and corresponding CPU state are restored in response to the request to transition the inference instance into a ready context. The restored state may include, by way of example, model-related data, runtime metadata, host-memory contents, pinned host-memory contents, GPU-memory contents, executable kernels, communication state, and other information used to resume or activate model execution.

[0123] In some embodiments, improved context switching is achieved through staged startup, snapshot-based restoration, pre-positioning of state in host memory, pre-creation of standby inference instances, pre-allocation of selected memory resources, or combinations thereof. Because the disclosed system can avoid re-performing at least portions of container bring-up, framework initialization, model loading, and GPU-state reconstruction on the critical path of request handling, the time required to transition from a non-serving or partially prepared context to a serving context can be materially reduced. This can enable a GPU-based inference platform to maintain low idle accelerator utilization while still providing faster readiness when a request is directed to a model thatPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) is not currently active in GPU memory. Accordingly, the disclosed embodiments can improve responsiveness, reduce cold-start delay, and increase efficiency in multi-model, multi-tenant, and on-demand inference environments.

[0124] Embodiments of the disclosed system perform context switching using a two-phase method in which at least a portion of startup work is performed before an inference request arrives and remaining restoration work is performed after the inference request is received. The two-phase method can reduce request-path latency by separating operations that do not require immediate GPU occupancy from operations that are performed to place a selected inference instance into a ready-to-serve state. As used herein, the first phase may be referred to as a preparation phase, pre-start phase, or standby phase, and the second phase may be referred to as a restore phase, activation phase, or request-driven phase.

[0125] Referencing Fig. 2, an example process 200 for performing fast context switching using a staged startup arrangement is depicted. As shown, the process 200 includes a pre-start phase 202 and a restore phase 204. In some embodiments, the pre-start phase 202 is perfomred before an inference request is received, and the restore phase 204 is performed in response to the inference request or in response to a determination that an inference instance is to be transitioned to a ready-to-serve state.

[0126] In some embodiments, the pre-start phase 202 includes preparing an inference instance to a standby state without maintaining the inference instance as fully active in GPU memory. For example, the pre-start phase 202 may include starting or partially starting a container runtime, establishing at least part of a virtual machine or execution environment, generating or accessing a snapshot associated with an initialized inference instance, loading snapshot data into accessible storage or host memory, and creating one or more standby inference instances associated with a model. In this manner, selected initialization operations may be performed ahead of time so that such operations do not need to be repeated on the critical path after a request arrives.

[0127] In some embodiments, the restore phase 204 includes restoring remaining execution state needed to transition the inference instance from the standby state to a ready state for serving the inference request. For example, the restore phase 204 may include selecting a standby inference instance associated with a requested model, restoring CPU-resident state, restoring GPU -resident state into GPU memory, assigning or activating memory resources, and completing runtime association needed for inference execution. After completion of the restore phase 204, the inference instance may be ready to process the inference request using the selected model.

[0128] In some embodiments, the two-phase method may employ the use of one or more snapshots to divide startup work between a pre-start phase and a restore phase. For example, during the prestart phase, the system may generate, access, cache, or otherwise prepare a snapshot corresponding toPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) an inference instance that has already completed at least part of an initialization sequence. During the restore phase, the system may use the snapshot to restore CPU state, GPU state, or both, so as to transition a standby inference instance to a ready state for serving an inference request. In this manner, the snapshot can preserve previously established execution state and allow at least a portion of startup work to be shifted away from the request-handling path.

[0129] As used herein, a snapshot may refer to a captured representation of execution state associated with an inference container instance, a model-serving process, a virtual machine, or another execution environment, where the captured representation is sufficient to facilitate later restoration of that execution environment to a partially initialized, initialized, standby, or ready state. In some embodiments, the snapshot represents the state of an inference instance after at least part of a startup sequence has already been completed, such that the inference instance need not be rebuilt entirely from an uninitialized condition when later activated. The snapshot may preserve information reflecting a previously established execution context so that at least some startup operations can be avoided, shortened, or shifted away from the critical path of request handling.

[0130] In some embodiments, the snapshot includes CPU state and GPU state. The CPU state may include, for example, process state, thread state, runtime metadata, virtual machine state, container-related state, host-memory contents, pinned host-memory contents, communication state, scheduling state, file mappings, library state, framework state, and other information associated with operation of the inference container instance on one or more host processors. The GPU state may include, for example, device-memory contents, model weights, tensors, buffers, key-value cache structures, activation-related data, executable kernels, driver-visible objects, communication objects, and other information associated with execution on one or more GPUs. In some embodiments, the snapshot may further include identifiers, mappings, descriptors, dependency information, version infomation, and restoration metadata that indicate how preserved CPU state and preserved GPU state are to be reassociated during a later restore operation.

[0131] In some embodiments, the snapshot may correspond to a point-in-time capture taken after selected initialization operations have been completed. For example, the snapshot may be created after container bring-up, after framework initialization, after model loading, after allocation of selected memory structures, after establishment of communication resources, or after another intermediate or advanced startup state has been reached. Accordingly, the snapshot need not be limited to a final ready-to-serve state. Instead, different snapshots may be generated at different stages of initialization depending on how much work is to be shifted into a pre-start phase and how much work is to remain for a request-driven restore phase. In some embodiments, a first snapshotPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) may correspond to a relatively early startup stage, while a second snapshot may correspond to a more fully initialized stage having additional CPU-resident state, GPU-resident state, or both.

[0132] In some embodiments, the snapshot may be stored in one or more forms and in one or more locations. For example, at least a portion of the snapshot may be stored in local storage, file-backed storage, host memory, a blob store, a distributed storage service, or another data store accessible to a GPU node 102. In some embodiments, different portions of the snapshot may be stored separately. For example, CPU-related portions of the snapshot may be stored in one location, while GPU-related portions of the snapshot may be stored in another location or represented by data structures optimized for later transfer into GPU memory. In some embodiments, the snapshot may be compressed, segmented, chunked, deduplicated, indexed, or otherwise organized to facilitate prewarming, transport, caching, or restoration. Thus, a snapshot need not be limited to a single monolithic file or image, but may instead include any preserved collection of state data and associated metadata from which at least part of a prior execution context can be reestablished.

[0133] In some embodiments, the snapshot is usable to restore the same inference container instance from which it was created or to instantiate a different inference container instance derived from the same captured state. For example, a standby inference container instance may be associated with a snapshot and later transitioned to a ready state by restoring at least part of the preserved CPU state and GPU state. In some embodiments, multiple standby inference container instances may be created using a common snapshot source, thereby allowing the same prepared state to support scale-out or repeated activation without requiring full reinitialization for each instance. In such embodiments, instance-specific state, such as network identity or runtime assignment information, may be established separately from the shared snapshot content.

[0134] In some embodiments, use of the snapshot reduces startup latency because the system is not required to recreate all runtime state from the beginning after an inference request is received. For example, rather than repeating container startup, framework initialization, memory preparation, model loading, and GPU-state establishment on demand, the system may restore a previously captured snapshot that already reflects completion of at least some of those operations. The amount of latency reduction may depend on the stage at which the snapshot was captured, the amount of state preserved in the snapshot, the speed with which the preserved state can be accessed, and the amount of additional work remaining to place the inference instance into a ready-to-serve condition. Accordingly, a snapshot may serve as an intermediate restoration artifact that enables staged startup, fast context switching, and reduced cold-start delay in a GPU-based inference environment.

[0135] In some embodiments, a snapshot may be created by launching an inference instance, allowing the inference instance to proceed through at least part of its initialization sequence, and thenPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) capturing a point-in-time representation of its execution state. For example, the captured state may include CPU state, GPU state, and associated restoration metadata. The captured state may then be stored in one or more files, in memory, or in another accessible data store for later restoration.

[0136] In some embodiments, the pre-start phase 202 may include a plurality of operations.Referencing Fig. 3, example operations that may be part of the first phase 202 are depicted. In some embodiments, the first phase 202 may include selecting a model 302 for standby preparation. In some cases, selecting a model may include predictive logic.. For example, the system may evaluate whether a model has been uploaded, deployed, newly made available, previously associated with recent demand, or otherwise identified as likely to receive an inference request within an upcoming interval. In some embodiments, the predictive logic may determine that the model 302 is to be prepared proactively before demand is fully known, rather than waiting until a request has already been routed to the model. The predictive logic may therefore be used to decide which model is to receive pre-start processing so that standby preparation is concentrated on models that are more likely to benefit from reduced startup latency. In some embodiments, selection of the model 302 may be based on anticipated traffic, expected service activity, deployment-related events, system policy, or other criteria indicating that the model should be placed into a standby-ready condition. For example, when the system determines that a particular model is likely to be requested soon, the system may begin preparing an inference container instance corresponding to the model 302 before receipt of the inference request.

[0137] The first phase 202 may further include launching an inference container 304 for the selected model. As used herein, the inference container 304 may refer to an isolated software execution environment configured to host inference execution for a model. In some embodiments, the inference container 304 may include or be associated with a container runtime, a virtual machine environment, a model-serving process, executable code, framework libraries, runtime metadata, configuration information, memory mappings, communication objects, and model-related resources used to execute inference operations. Thus, the inference container 304 need not be limited to a lightweight application package, but may more broadly correspond to any runtime-isolated execution instance in which model-serving software is launched and prepared for later activation.

[0138] In some embodiments, launching the inference container 304 may include starting or partially starting the container runtime. In some embodiments, the launched inference container 304 may be brought through at least part of an initialization sequence so that an execution environment can be established before receipt of an inference request. In some embodiments, the inference container 304 is launched during the first phase 202 so that at least part of the software environment needed for inference serving already exists before demand is known. In this manner, the system canPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) avoid repeating full container bring-up, framework initialization, and related startup operations after the inference request arrives.

[0139] In some embodiments, the first phase 202 may further include configuring process state 306. Configuring process state 306 may include establishing runtime metadata, preparing CPU-resident memory structures, initializing framework components, or otherwise preparing the inference container for later restoration. The first phase 202 may also include accessing a snapshot 308 associated with the inference container. As described herein, the snapshot may correspond to a previously captured execution state and may include CPU state, GPU state, and associated restoration metadata. In some embodiments, the first phase 202 may further include pre-positioning the snapshot 310. Pre-positioning the snapshot 310 may include placing at least a portion of snapshot data in accessible storage, local storage, file-backed storage, or host memory so that the snapshot data is available for later restoration. The first phase 202 may also include maintaining a standby state 312, in which a standby inference container instance is preserved in a prepared but not fully active state pending receipt of an inference request. In some embodiments, the first phase 202 may further include associating metadata 314 with the standby inference container instance. The metadata may identify, map, or otherwise relate the standby inference container instance to the snapshot and to restoration information usable during a later restore phase.

[0140] In some embodiments, not all operations associated with the first phase 202 need to be performed in every implementation. For example, one or more of the operations described with respect to the first phase 202 may be omitted, combined, reordered, repeated, or performed in a different manner depending on the model, runtime environment, available snapshot state, system policy, or deployment condition. Thus, the first phase 202 may include fewer than all of the illustrated operations while still preparing an inference instance for later restoration or activation.

[0141] Referencing Fig. 4, example processes that may be part of the second phase 204 are depicted. In some embodiments, the second phase 204 may include processing an inference request 402. Processing the inference request 402 may include receiving the inference request, determining a model associated with the inference request, and selecting a standby inference container instance corresponding to the model.

[0142] In some embodiments, the second phase 204 may further include restoring CPU state 404. Restoring CPU state 404 may include restoring or rebinding process state, thread state, runtime metadata, host-memory contents, pinned host-memory contents, communication state, scheduling state, file mappings, library state, framework state, or other CPU-resident information associated with operation of the inference container instance. The second phase 204 may also include restoring GPU memory state 406. Restoring GPU memory state 406 may include loading model weights,PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) tensors, buffers, key-value cache structures, activation-related data, executable kernels, driver-visible objects, communication objects, or other GPU-resident state into GPU memory from host memory, local storage, file-backed storage, or a snapshot blob store.

[0143] In some embodiments, the second phase 204 may further include associating runtime data 408. Associating runtime data 408 may include re-associating identifiers, mappings, descriptors, dependency information, version information, restoration metadata, or other runtime information so that preserved CPU state and preserved GPU state are properly related for resumed execution. The second phase 204 may also include activating a container 410. Activating the container 410 may include transitioning the standby inference container instance from a prepared or standby condition to a ready state for inference serving so that the inference request can be processed using the restored model state.

[0144] In some embodiments, not all operations associated with the second phase 204 need be performed in every implementation. For example, one or more of the operations described with respect to the second phase 204 may be omitted, combined, reordered, repeated, or performed in a different manner depending on the model, runtime environment, available snapshot state, memory availability, GPU configuration, system policy, or deployment condition.

[0145] Referencing Fig. 5, an example system architecture for standby inference preparation and request-driven activation is depicted. In some embodiments, may include one or more GPU nodes 500 configured to support staged startup of model-serving execution environments so that at least part of startup processing can be performed before an inference request is received and remaining restore processing can be performed after the inference request is received. As shown, the GPU node 500 may maintain one or more inference container instances in different readiness conditions. For example, the GPU node 500 may include an inference container standby 502 representing a prepared but not fully active inference instance and an inference container warm 504 representing an inference instance that has been transitioned closer to or into a ready-to-serve condition. In some embodiments, the inference container standby 502 may include or be associated with a container runtime 508, and the inference container warm 504 may include or be associated with a container runtime 506. As described herein, each inference container may correspond to an isolated execution environment configured to host model-serving software for inference execution and may include or be associated with a container runtime, a virtual machine environment, executable code, framework components, runtime metadata, model-related resources, memory structures, and communication objects used during inference serving.

[0146] In some embodiments, the inference container standby 502 may correspond to a container instance that has already proceeded through at least part of an initialization sequence but is not yetPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) occupying the full GPU-resident execution state needed for active inference serving. For example, the inference container standby 502 may have an active or partially active software environment while selected GPU state remains absent from GPU memory until a later restore operation is performed. In this manner, the inference container standby 502 may preserve at least part of the startup work completed during the first phase while avoiding continued occupation of GPU execution resources during idle periods. By contrast, the inference container warm 504 may correspond to an inference instance for which additional execution state has been restored so that the inference instance is able to process an inference request with reduced startup delay. In some embodiments, transition from the inference container standby 502 to the inference container warm 504 may include restoring CPU-resident state, restoring GPU-resident state, re-associating runtime metadata, assigning or activating memory resources, and otherwise completing operations needed to place the inference instance into a ready state.

[0147] In some embodiments, a GPU node 500 further includes a node agent 510 configured to coordinate node-level operations associated with standby preparation, snapshot access, restore processing, and resource management. For example, the node agent 510 may coordinate creation of standby inference container instances, association of snapshots with corresponding inference container instances, pre-positioning of snapshot data, and request-driven restoration of preserved execution state. In some embodiments, the node agent 510 may communicate with a snapshot store 512 to access snapshot data, restoration metadata, or other preserved execution state associated with a selected model. The snapshot store 512 may include local storage, remote storage, file-backed storage, host memory, a blob store, or another data store from which preserved state may be obtained. In some embodiments, different portions of a snapshot may be stored in different locations, and the node agent 510 may coordinate retrieval of those portions for later restoration.

[0148] In some embodiments, the node agent 510 may further coordinate restoration of model-related state into one or more GPUs of the GPU node 500, such as GPU 0520, GPU 1 522, through GPU N 524. For example, the node agent 510 may manage GPU-memory allocation, initiate memory transfers from storage or host memory to GPU memory, coordinate restore operations across multiple GPUs, and provide restored GPU objects or memory associations to a selected inference container instance. Because the node agent 510 may have visibility across multiple GPUs on the GPU node 500, the node agent 510 may coordinate restore operations at a node-wide level rather than only within an individual container context. This arrangement may be beneficial where a selected model is to be restored using multiple GPUs or where resource availability across the GPU node 500 is to be considered during restoration and activation.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0149] In some embodiments, the GPU node 500 operates in conjunction with a scheduler 514 and a gateway 516. The gateway 516 may receive inference requests and route the inference requests toward a selected GPU node 500 or a selected inference container instance associated with a requested model. For example, the gateway 516 may serve as an ingress point through which inference traffic is received before being directed to infrastructure selected to service the request. In some embodiments, the scheduler 514 may determine placement of inference workloads across a plurality of GPU nodes, assign a model to a particular GPU node, initiate standby preparation for a selected model, or otherwise coordinate which GPU node is to maintain or activate a corresponding inference instance. The scheduler 514 may therefore participate in pre-request preparation as well as request-time placement decisions.

[0150] In some embodiments, the scheduler 514 and the gateway 516 may cooperate with the node agent 510 so that a model can be prepared proactively and later activated in response to demand. For example, the scheduler 514 may determine that a particular model is to be placed into a standbyready condition, and the node agent 510 on the GPU node 500 may create or maintain the inference container standby 502 and associate that standby inference container instance with snapshot data stored in the snapshot store 512. Later, when an inference request is received at the gateway 516 for the corresponding model, the request may be routed to the GPU node 500, and the node agent 510 may coordinate restoration of the preserved execution state so that the inference container standby 502 can be transitioned into the inference container warm 504 or another ready-to-serve state. In this manner, the architecture of FIG. 5 may support a workflow in which selected initialization operations are performed before request arrival and remaining restore operations are performed after the request arrives.

[0151] In some embodiments, the snapshot store 512 may store snapshot data representing previously captured execution state of an inference container instance after at least part of an initialization sequence has been completed. Such snapshot data may include CPU state, GPU state, and restoration metadata. For example, the preserved CPU state may include process state, thread state, runtime metadata, host-memory contents, pinned host-memory contents, framework state, library state, and communication state, while the preserved GPU state may include device-memory contents, model weights, tensors, buffers, key-value cache structures, activation-related data, executable kernels, driver-visible objects, and other GPU-resident information. In some embodiments, the node agent 510 may access the snapshot store 512 during a pre-warm operation to place at least part of the snapshot data into accessible storage or host memory before the corresponding inference request is received, thereby allowing later restoration to proceed more quickly when demand becomes known.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0152] In some embodiments, Fig. 5 further illustrates that multiple readiness conditions may coexist on the same GPU node 500. For example, one or more inference container instances may remain in standby form while one or more other inference container instances are already warm or active. This can allow the GPU node 500 to support multiple models, multiple tenants, repeated activations, or scale-out behavior without requiring each inference instance to be fully initialized from an uninitialized condition on the request path. In some embodiments, multiple standby inference container instances may be derived from a common snapshot source and may later be restored into corresponding warm or ready instances in response to respective inference requests. In this manner, the architecture of Fig. 5 can support repeated or parallel activation of inference instances while reusing previously prepared execution state.

[0153] Accordingly, Fig. 5 illustrates an example deployment architecture in which the scheduler 514, the gateway 516, the GPU node 500, the inference container standby 502, the inference container warm 504, the container runtimes 506 and 508, the node agent 510, the snapshot store 512, and the GPUs 520, 522, and 524 may operate together to reduce startup latency for inference serving. By allowing standby preparation to occur before request arrival and restore processing to occur after request arrival, the illustrated architecture can reduce repeated initialization overhead, reduce coldstart delay, and improve resource utilization in multi-model, multi-tenant, and on-demand GPU inference environments. Although Fig. 5 depicts particular components and relationships, the disclosed system is not limited to the illustrated arrangement, and one or more components may be omitted, combined, distributed, replicated, or implemented in another manner in various embodiments.

[0154] Referring to Fig. 6, a flowchart is shown of an example method for reducing startup latency or activation latency of a workload executed using accelerator hardware, such as one or more graphics processing units of a GPU node. In some embodiments, the method of Fig. 6 may be used for inference serving, including model-serving workloads executed in response to received requests. Although Fig. 6 illustrates three principal stages for ease of explanation, each illustrated stage may include one or more sub-operations, and the order, grouping, and allocation of the operations may vary in different embodiments.

[0155] At operation 602, the method may include preparing, before a request is received, a standby instance with a runtime environment and associating the standby instance with snapshot data including GPU state. In some embodiments, operation 602 may include initializing, on a host processor, an execution environment for a model-serving instance through at least a portion of a startup sequence that includes framework initialization and runtime configuration. The standby instance may be associated with a container runtime, a virtual machine environment, or anotherPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) execution environment configured for model serving. In some embodiments, after completion of at least a portion of the startup sequence and before serving any external request, a snapshot may be captured to preserve execution state of the model-serving instance. The snapshot data may include GPU state, GPU-related state data, CPU state, CPU-resident state, or combinations thereof. In some embodiments, the snapshot data may further include restoration metadata identifying associations between CPU-side state and GPU-side state to facilitate later restoration. Operation 602 may further include storing the snapshot in storage accessible to a GPU node, pre-warming the snapshot data in accessible storage, and pre-positioning at least a portion of the snapshot data in host memory before request arrival. In some embodiments, the standby instance may be maintained in a standby state without occupying GPU execution resources, without allocating GPU memory for the model-serving instance, and without loading GPU-resident state for the model-serving instance. In some embodiments, multiple standby instances may be created from a common snapshot source so that multiple instances may later be activated using corresponding preserved execution state.

[0156] At operation 604, the method may include restoring, after the request is received, the GPU state from the snapshot data into GPU memory and associating the GPU state with the standby instance to make the instance ready to execute. In some embodiments, operation 604 may begin in response to receiving a request to execute a model-serving workload and selecting the standby instance associated with that workload. In some embodiments, restoration may be coordinated by a node-level controller, such as a node agent of a GPU node, having visibility into host-side and device-side resources. The restoration may include restoring at least a portion of CPU state or CPU-resident state from the snapshot into the runtime environment of the standby instance and loading GPU state or GPU-related state data from the snapshot into GPU memory. In some embodiments, the GPU state may be transferred from host memory to GPU memory using parallel data loading across multiple GPUs. The loaded GPU state may be associated with the standby instance in accordance with the restoration metadata so that CPU-side state and GPU-side state are correctly re-associated during reactivation. In some embodiments, restoration at block 604 may be performed across one or more GPUs of the GPU node, including restoration of GPU-resident state across multiple GPUs for a multi-GPU inference instance. In this manner, operation 604 may transition the standby instance into a ready state for inference serving without repeating a full initialization sequence from an uninitialized condition.

[0157] At operation 606, the method may include executing the workload using the ready-to-execute instance. In some embodiments, execution of operation 606 may include activating the standby instance as a ready instance and using the ready instance to execute the requested modelserving workload on one or more GPUs. The workload may correspond to an inference request, aPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) prompt-based request, a generative-model request, or another model-execution task. Because the standby instance may have been prepared before request arrival and restored using preserved execution state after request arrival, the method of Fig. 6 may reduce cold-start latency, reduce repeated initialization overhead, and improve responsiveness of the underlying GPU-based inference platform.

[0158] Accordingly, Fig. 6 illustrates an example flow in which a standby instance is prepared in advance at operation 602, preserved execution state is restored after request receipt at block 604, and the workload is executed using the resulting ready instance at operation 606. In some embodiments, the operations of Fig. 6 may collectively include initialization of a model-serving execution environment on a host processor, capture of a snapshot preserving CPU-resident state and GPU-related state data, storage and pre-positioning of at least part of the snapshot in storage or host memory accessible to a GPU node, maintenance of the standby instance without allocated GPU execution resources, request -driven selection of the standby instance, node -level coordination of restoration into one or more GPUs, activation of the standby instance, and execution of the workload using the activated instance.

[0159] Referring to Fig. 7, a flowchart is shown of an example computer-implemented method for reducing activation latency of a model-serving workload on accelerator hardware. In some embodiments, the method of Fig. 7 may be performed by a GPU node executing a model-serving instance using one or more host processors and one or more GPUs. Although Fig. 7 depicts a particular sequence of operations for purposes of explanation, the illustrated operations may be combined, subdivided, reordered, repeated, or omitted in various embodiments.

[0160] At operation 702, the method may include initializing, on a host processor, an execution environment for a model-serving instance through at least a portion of a startup sequence that includes framework initialization and runtime configuration. In some embodiments, initializing the execution environment may comprise launching a container runtime or virtual machine environment and loading one or more model-serving framework libraries. The execution environment may further include establishing runtime metadata, configuring communication resources, and preparing one or more processes or threads associated with the model-serving instance.

[0161] At operation 704, the method may include capturing, after completion of the at least a portion of the startup sequence but prior to serving any external request, a snapshot that preserves execution state of the model-serving instance. In some embodiments, the snapshot may be captured after model parameters have been loaded into memory but before any inference request is processed by the model-serving instance. The snapshot may comprise CPU-resident state and GPU-related state data and may include restoration metadata that identifies associations between the CPU-resident statePATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) and the GPU-related state data. In some embodiments, the GPU-related state data may comprise one or more of model weights, executable kernels, tensor buffers, activation buffers, or key-value cache structures. In some embodiments, the snapshot may be generated at a plurality of different initialization checkpoints, and the method may further comprise selecting, for restoration, a snapshot corresponding to a desired readiness level.

[0162] At operation 706, the method may include storing the snapshot in storage accessible to a GPU node and pre-positioning at least a portion of the snapshot in host memory of the GPU node. In some embodiments, pre-positioning the snapshot may comprise loading at least a portion of the snapshot into host memory while another portion of the snapshot remains stored in persistent storage. In this manner, preserved execution state may be made available for restoration while reducing the amount of data retrieval required after a request is received.

[0163] At operation 708, the method may include maintaining a standby instance of the modelserving instance in the execution environment without allocating GPU memory for the modelserving instance and without loading GPU-resident state for the model-serving instance. In some embodiments, the standby instance may remain associated with the execution environment and with the preserved snapshot while GPU execution resources are not occupied by that standby instance. Maintaining the standby instance without allocating GPU memory may permit the GPU node to reassign GPU execution resources to a different workload while the standby instance is maintained. In some embodiments, multiple standby instances may be maintained from a common snapshot, and respective standby instances may later be independently activated in response to respective requests.

[0164] At operation 710, the method may include receiving a request to execute the model-serving workload and selecting, in response to the request, the standby instance associated with the modelserving workload. In some embodiments, selecting the standby instance may be based at least in part on predictive logic indicating an anticipated likelihood of receiving the request. For example, the predictive logic may be used to determine which standby instance is to be maintained or selected for a corresponding workload.

[0165] At operation 712, the method may include coordinating, by a node-level controller, restoration of preserved execution state. In some embodiments, the node-level controller may manage GPU memory allocation across a plurality of GPUs before loading the GPU-related state data. The restoration may include restoring at least a portion of the CPU-resident state from the snapshot into the execution environment of the standby instance and loading the GPU-related state data from the snapshot into GPU memory of one or more GPUs of the GPU node. In some embodiments, restoring the GPU-related state data may comprise transferring the GPU-related state data into GPU memory using parallel data transfers to multiple GPUs. In some embodiments,PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) restoring the CPU-resident state and loading the GPU-related state data may be performed concurrently to reduce activation latency. The loaded GPU-related state data may be associated with the standby instance in accordance with the restoration metadata so that the restored CPU-resident state and the restored GPU-related state correspond to one another.

[0166] At operation 714, the method may include activating the standby instance as a ready instance and executing the model-serving workload using the ready instance on the one or more GPUs. In some embodiments, activating the standby instance may comprise re-associating restored CPU-resident state and GPU-related state using the restoration metadata. The ready instance may then execute the requested workload without repeating a full cold initialization sequence. In some embodiments, executing the model-serving workload may comprise performing inference using a large language model to generate output tokens responsive to a prompt included in the request. More generally, the ready instance may execute another model-serving workload using the one or more GPUs after restoration of the preserved execution state.

[0167] Accordingly, Fig. 7 illustrates a staged activation process in which an execution environment is initialized at operation 702, a snapshot preserving CPU-resident state, GPU-related state data, and restoration metadata is captured at operation 704, the snapshot is stored and at least partially pre-positioned at operation 706, a standby instance is maintained without allocated GPU memory at operation 708, a request is received and a corresponding standby instance is selected at operation 710, restoration is coordinated by a node-level controller at operation 712, and the standby instance is activated as a ready instance for workload execution at operation 714. In some embodiments, the method of Fig. 7 may reduce request-path startup latency relative to a cold initialization by avoiding repeated framework initialization and model loading after receipt of the request.

[0168] In some embodiments, the disclosed system may support execution of a model-serving workload across multiple GPUs while maintaining isolation between tenants at the GPU-communication level. For example, a model may be distributed across two or more GPUs of a GPU node such that model weights, activation-related data, tensor data, cache data, or other execution state is stored or processed across multiple GPU memories. In such embodiments, the system may preserve isolation of tenant-associated data even where the workload relies on inter-GPU communication to execute a multi-GPU model. This arrangement may be beneficial in multi-tenant environments in which separate tenants share GPU-node infrastructure but are to remain logically and operationally isolated from one another.

[0169] In some embodiments, the execution environment for the model-serving workload may include a virtual machine environment, a container runtime, or a combined container-and-virtual-PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) machine arrangement. For example, the disclosed runtime may be implemented using a virtual-machine-based execution environment having an interface compatible with a host operating environment, thereby allowing model-serving software to execute within an isolated environment while still participating in snapshot-based preparation and restore operations. In some embodiments, GPU virtualization may be used so that GPU-related state can be preserved, restored, and associated with a selected standby instance while maintaining separation among different execution environments associated with different tenants.

[0170] In some embodiments, isolation of multi-GPU execution may include controlling communication paths used between GPUs and between a GPU node and other nodes. For example, where GPUs communicate using a GPU-to-GPU interconnect such as NVLink or another peer-to-peer interconnect, or where node-to-node transfers use RDMA-capable networking, the disclosed system may isolate communication resources so that a first tenant-associated execution environment is prevented from improperly accessing GPU communication state, data paths, or transferred data associated with a second tenant-associated execution environment. In this manner, GPU isolation may extend beyond allocation of compute cycles and memory regions and may further include isolation of interconnect-mediated communication used during distributed or multi-GPU execution.

[0171] In some embodiments, a node-level controller, such as a node agent, may participate in the isolation and restoration process because the node-level controller may have visibility across multiple GPUs of the GPU node. For example, the node agent may manage GPU memory allocation, coordinate transfer of GPU-related state into GPU memory across multiple GPUs, and control restoration or activation of a multi-GPU inference instance at a node-wide level rather than only within an individual execution context. This arrangement may be beneficial where a selected model spans multiple GPUs and where restoration or activation is to occur without exposing GPU-resident state, communication state, or interconnect usage associated with one tenant to another tenant.

[0172] In some embodiments, the disclosed architecture may support a runtime mediation layer for multi-GPU communication libraries or APIs used by a model-serving instance. For example, a communication call associated with inter-GPU coordination may be intercepted, evaluated, translated, forwarded, or otherwise mediated by software associated with the execution environment, the virtual machine environment, the node agent, the GPU virtualization layer, or another trusted control component before corresponding communication resources are used. In some embodiments, such mediated communication may help enforce tenant isolation policies for GPU-to-GPU communication paths, including communication over NVLink, similar peer-to-peer interconnects, RDMA-capable communication paths, or combinations thereof. Although a particularPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) communication library may be used in some implementations, the disclosed subject matter is not limited to any specific library, API, or vendor-specific software interface.

[0173] In some embodiments, by isolating GPU communication resources in addition to compute and memory resources, the disclosed system may support secure or policy-controlled execution of multi-GPU models in a multi-tenant environment. For example, a first tenant may execute a multiGPU model using a first isolated execution environment while a second tenant executes another workload using a second isolated execution environment on the same or another GPU node, with communication resources, execution state, and restored GPU-related state remaining separated according to tenant boundaries. In this manner, the disclosed system may support multi-GPU model execution together with snapshot-based activation, virtual-machine-based isolation, GPU virtualization, and controlled use of GPU interconnect and networking resources.

[0174] Referring to Fig. 8, one example software architecture of a node is shown for providing tenant isolation in a multi-tenant, multi-GPU computing environment. In some embodiments, Fig. 8 illustrates a node-level software stack that may be deployed in, integrated with, or used by a node agent, and in which communication-library calls associated with multi-GPU execution are mediated across guest-side and host-side software layers to enforce tenant-specific isolation policies. In some embodiments, at least portions of the software architecture may be implemented on or based on a Linux kernel environment, although other operating-system kernels or kernel architectures may be used in various embodiments. This arrangement may be beneficial where multiple tenants share GPU-node infrastructure and where GPU-to-GPU and GPU-network communication paths are to remain isolated while still permitting execution of multi-GPU models.

[0175] In some embodiments, Fig. 8 further illustrates separation among a guest user space, a guest kernel space, a host user space, and a host kernel space such that communication operations associated with multi-GPU execution may be intercepted, checked, and selectively forwarded across trust boundaries. This arrangement may be beneficial in multi-tenant deployments in which GPU-to-GPU communication paths, such as NVLink communication paths, and GPU-network communication paths, such as RDMA-capable communication paths, are to be controlled on a pertenant basis while still permitting execution of multi-GPU models.

[0176] In some embodiments, the node software architecture may be separated into a guest user space, a guest kernel space, a host user space, and a host kernel space so that access to communication resources is controlled across distinct software layers. The guest user space may correspond to a tenant-facing execution layer in which a workload process runs and issues communication requests, but in which direct access to lower-level inter-accelerator and network communication resources may be restricted. The guest kernel space may correspond to a guest-sidePATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) system layer that permits intercepted requests to be forwarded out of the tenant -facing execution context toward a trusted host-side path. The host user space may correspond to a trusted control layer having access to host-side runtime and policy-enforcement functionality used to determine whether a requested communication operation is authorized. The host kernel space may correspond to a privileged system layer having access to kernel services, drivers, device interfaces, and other hostlevel resources used to carry out an authorized communication operation. In this manner, access to communication resources may be separated such that a tenant workload may request communication operations without being given unrestricted direct access to communication paths or resources allocated to other tenants.

[0177] In the guest user space, a user inference process 802 may execute a model-serving workload associated with a tenant or other isolated execution context. In some embodiments, the user inference process 802 may invoke communication-library functions used to coordinate execution across multiple GPUs. A communications library 806 (e.g., NVIDIA Collective Communications Library (NCCL)) may be positioned between the user inference process 802 and lower-level runtime components such that calls issued by the user inference process 802 are first intercepted by the NCCL call intercept library 806. In some embodiments, the NCCL call intercept library 806 may identify, classify, translate, annotate, or otherwise prepare a communication call for policy enforcement before the call is permitted to access lower-level GPU communication resources.

[0178] In the guest kernel space, a container runtime 808 may receive a call or request forwarded from the NCCL call intercept library 806. In some embodiments, the container runtime 808 may form part of a virtual-machine-based secure container runtime configured to support isolated execution of model-serving instances. The container runtime 808 may therefore serve as an interface through which an intercepted NCCL-related call is passed from the guest-side execution environment toward a trusted host-side control path. In some embodiments, forwarding the call through the container runtime 808 may permit communication operations associated with a multi-GPU model to be mediated outside the guest user process while preserving tenant isolation.

[0179] In the host user space, a container runtime host 812 may receive information corresponding to the forwarded call and may perform host-side runtime handling for the request. In some embodiments, the container runtime host 812 may include or cooperate with a call security check 810 configured to determine whether the requested communication operation is authorized. For example, the call security check 810 may verify that the call corresponds to an allowed tenant context, an allowed GPU allocation, an allowed inter-GPU path, an allowed remote communication path, or another permitted communication policy before the call is executed. In some embodiments, the call security check 810 may thereby isolate usage of GPU interconnect resources and remotePATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) direct memory access resources across multiple tenants. The trusted host-side runtime path may therefore prevent one tenant-associated inference process from improperly using or accessing GPU communication resources allocated to another tenant-associated process.

[0180] After authorization, the call may be provided to an NCCL library 804 for execution using the permitted communication resources. In some embodiments, the NCCL library 804 may implement one or more collective or peer communication functions used for coordination across multiple GPUs participating in execution of a distributed model. The NCCL library 804 may communicate with a host kernel 814 (e.g., Kernel-based Virtual Machine (KVM)) to access underlying driver functionality, kernel services, device interfaces, memory-registration services, networking services, or other host-level resources used to carry out the requested operation. In some embodiments, the host kernel 814 may cooperate with GPU drivers, interconnect drivers, or networkinterface drivers to establish or use NVLink paths, RDMA-capable paths, or other communication paths needed for multi-GPU execution.

[0181] Accordingly, Fig. 8 illustrates an example node software architecture in which the user inference process 802 issues a communication request, the NCCL call intercept library 806 intercepts the request in the guest user space, the container runtime 808 forwards the request through the guest kernel space, the container runtime host 812 and the call security check 810 perform host-side mediation and authorization, the NCCL library 804 executes an approved communication operation, and the host kernel 814 provides kernel-level support for the operation. In some embodiments, this layered arrangement provides for tenant isolation and secure support for multi-GPU models by isolating inter-GPU and network communication usage across tenants while allowing authorized communication-library calls to proceed through a trusted runtime path.

[0182] In some embodiments, an isolation policy may comprise a tenant-specific or executioncontext-specific set of rules used to control whether a communication request associated with multiaccelerator execution is permitted to access communication resources of a node. The isolation policy may be applied in a trusted host-side control path so that communication operations associated with a workload process are checked outside the guest user process before corresponding inter-accelerator or network communication resources are used. In some embodiments, the isolation policy may be used to preserve separation among different tenant-associated execution environments while still permitting authorized execution of a distributed or multi-GPU model.

[0183] In some embodiments, the isolation policy may be created, defined, selected, instantiated, stored, maintained, or otherwise provided by software associated with the execution environment, the virtual machine environment, the node agent, the GPU virtualization layer, the container runtime host, or another trusted control component. For example, a tenant-associated execution environmentPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) may be associated with a corresponding isolation policy that is consulted when a communicationlibrary call is intercepted and forwarded for host-side evaluation. In some embodiments, the isolation policy may be enforced by a call security check or another policy-enforcement component operating in a trusted host-side software layer.

[0184] In some embodiments, the isolation policy may include one or more permitted communication conditions, allowed communication contexts, disallowed communication contexts, or other authorization criteria. For example, the isolation policy may specify an allowed tenant context, an allowed accelerator allocation, an allowed inter-accelerator path, an allowed remote communication path, or another permitted communication policy. In some embodiments, the isolation policy may indicate which GPUs or other accelerators are allocated to a particular tenant-associated workload, which peer-to-peer or switched interconnect paths are permitted for that workload, whether a requested communication path corresponds to an authorized NVLink path or other inter-accelerator path, and whether a requested remote path corresponds to an authorized RDMA-capable path or another permitted network communication path. In some embodiments, the isolation policy may thereby prevent one tenant-associated inference process from improperly using or accessing GPU communication resources allocated to another tenant-associated process.

[0185] In some embodiments, the isolation policy may further include criteria identifying permitted communication-library operations, permitted groups of participating accelerators, permitted host-level resources, permitted driver-level resources, or combinations thereof. In some embodiments, the isolation policy may be used together with runtime mediation, GPU virtualization, container-runtime processing, and host-kernel support so that GPU-to-GPU communication usage and GPU-network communication usage remain isolated across tenants while authorized communication operations are allowed to proceed through a trusted runtime path.

[0186] Fig. 9 illustrates a flowchart of an example method for providing tenant isolation in a multitenant, multi -accelerator computing environment. In some embodiments, the method of Fig. 9 may be performed by a computing node having a separated guest user space, guest kernel space, host user space, and host kernel space, such as the node software architecture described with reference to Fig.8. As shown in Fig. 9, the method may begin at operation 902 by executing, in the guest user space, by a workload process, a workload associated with a tenant and issuing, by the workload process, a communication request associated with use of one or more accelerators of the node. In some embodiments, the workload may comprise a model-serving workload, such as execution of a distributed inference model across multiple GPUs or other accelerators. The communication request may comprise a communication-library call used to coordinate execution across multiple accelerators participating in the distributed workload.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0187] At operation 904, the communication request may be intercepted in the guest user space. In some embodiments, intercepting the communication request may comprise intercepting a communication-library call. In some embodiments, the interception may be performed by a communication-call intercept library in the guest user space, such as an intercept layer associated with a collective communications library. The intercept library may identify, classify, translate, annotate, log, or otherwise prepare the communication request for policy enforcement before the communication request is permitted to access lower-level communication resources. In this manner, communication operations associated with the workload process may be mediated before the workload process directly uses inter-accelerator or network communication paths.

[0188] At operation 906, the communication request may be forwarded through the guest kernel space to the host user space for evaluation. In some embodiments, forwarding the communication request may be performed by a container runtime in the guest kernel space. For example, the container runtime may receive the intercepted communication request from the guest-side execution environment and may pass the communication request toward a trusted host-side control path for further processing. This forwarding arrangement may preserve tenant isolation by moving authorization and communication mediation outside the workload process while still permitting the workload to request coordinated execution across multiple accelerators.

[0189] At operation 908, the communication request may be evaluated in the host user space based at least in part on an isolation policy to determine whether the communication request is authorized. In some embodiments, evaluating the communication request may be performed in the host user space by a call security check. The call security check may evaluate whether the communication request corresponds to an allowed tenant context, an allowed accelerator allocation, an allowed interaccelerator path, an allowed remote communication path, or another permitted communication policy. For example, the call security check may determine whether the requesting tenant is authorized to use a specified set of GPUs, whether communication between particular accelerators is permitted, whether a requested peer-to-peer path is authorized, and whether a requested network path satisfies isolation constraints. In some embodiments, the allowed inter-accelerator path may comprise an NVLink path, and the allowed remote communication path may comprise a remote direct memory access-capable path.

[0190] At operation 910, responsive to determining that the communication request is authorized, a communication operation may be executed using communication resources of the computing node. In some embodiments, executing the communication operation may be performed by a communication library in the host user space. The communication library may implement collective communication functions, peer communication functions, or other accelerator-coordination functionsPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) used to support execution of the distributed workload. The communication resources used for the communication operation may include inter-accelerator communication resources, network communication resources, or both. In some embodiments, the inter-accelerator communication usage may comprise NVLink communication usage.

[0191] At operation 912, kernel-level support for the communication operation may be provided in the host kernel space. In some embodiments, providing the kernel-level support may comprise providing, by a host kernel, access to host-level resources used to carry out the communication operation. Such host-level resources may include driver functionality, kernel services, device interfaces, memory-registration services, networking services, accelerator-driver resources, interconnect-driver resources, or network-interface resources. The host kernel may cooperate with one or more drivers or kernel services to establish, manage, or use authorized communication paths needed for the communication operation.

[0192] At operation 914, inter-accelerator communication usage and network communication usage may be isolated across tenants while execution of a distributed workload is permitted. In some embodiments, the sequence of operations 902-914 may thereby allow a tenant-associated workload process to request and use authorized communication operations without permitting unauthorized access to communication resources allocated to another tenant. Although Fig. 9 shows one example order of operations, the operations may be combined, subdivided, repeated, omitted, or performed in different orders in various embodiments, and one or more of the operations may be carried out by software, firmware, hardware, or any suitable combination thereof.

[0193] In some embodiments, startup latency associated with deploying a model-serving instance may be reduced by loading or reconstructing model state using accelerator-to-accelerator transfer operations rather than independently loading a complete model image into each accelerator from host storage or host memory. A model snapshot may refer to any stored representation of model state sufficient to initialize execution of a model, including model weights, parameters, tensor data, runtime artifacts, cached compilation products, metadata, execution graphs, memory images, or combinations thereof. In some embodiments, a snapshot may first be loaded into a first accelerator, and at least a portion of the snapshot may then be copied from the first accelerator to one or more additional accelerators over a high-bandwidth inter-accelerator interconnect, thereby reducing repeated host-side transfer operations and decreasing time to service readiness.

[0194] In some embodiments, the inter-accelerator interconnect may comprise NVLink or another accelerator-to-accelerator communication fabric providing direct or relatively efficient transfer between accelerators. Equivalent interconnects may include any peer-to-peer accelerator communication mechanism, switched accelerator fabric, coherent accelerator interconnect, directPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) device-memory transfer path, or other high-bandwidth path that permits copying data between accelerators with lower overhead than repeated loading from host-side resources. By using such an interconnect, a computing node may replicate a model snapshot across multiple GPUs or other accelerators after an initial load, thereby reducing model load time and startup latency for distributed inference or other multi-accelerator workloads.

[0195] In some embodiments, a snapshot may be copied from a source accelerator to multiple destination accelerators in parallel, in sequence, or according to a hierarchical distribution pattern. For example, a first accelerator may receive a snapshot from host memory or persistent storage, and the first accelerator may then transmit the snapshot, or one or more portions thereof, to a second accelerator and a third accelerator. In some embodiments, the second accelerator may further forward the snapshot to one or more additional accelerators, such that snapshot distribution is performed as a fan-out operation, tree-based operation, pipelined operation, ring-based operation, mesh-based operation, or other multi-hop distribution operation. Such arrangements may reduce contention on host-side buses, memory channels, storage interfaces, or networking resources that might otherwise be used to independently load the same snapshot into each accelerator.

[0196] In some embodiments, the copied snapshot may comprise a complete model image or a partial model image. For example, the copied data may include one or more layers, shards, partitions, tensor groups, parameter ranges, key-value caches, executable kernels, runtime buffers, or initialization structures associated with the model. In some embodiments, different accelerators may receive identical copies of the snapshot. In other embodiments, different accelerators may receive different portions of the snapshot, such as in connection with model parallelism, tensor parallelism, pipeline parallelism, expert partitioning, sharded execution, or other distributed execution arrangements. Accordingly, copying a snapshot between accelerators may include replication, partitioned distribution, selective synchronization, incremental update propagation, or combinations thereof.

[0197] In some embodiments, the system may determine whether to use host-to-accelerator loading, accelerator-to-accelerator copying, or a hybrid loading strategy based on one or more runtime conditions. Such runtime conditions may include accelerator topology, interconnect bandwidth, host memory bandwidth, storage bandwidth, snapshot size, number of accelerators to be initialized, current node utilization, model placement, tenancy constraints, or service-level objectives. For example, where a node includes multiple accelerators connected by NVLink, the system may load a snapshot into a subset of accelerators and then use NVLink transfers to populate remaining accelerators. In other embodiments, different portions of a snapshot may be loaded fromPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) different sources, such as loading a first portion from host memory and obtaining a second portion from a peer accelerator.

[0198] In some embodiments, accelerator-to-accelerator copying may be used during cold start, warm start, failover recovery, workload migration, autoscaling events, replica creation, or rehydration of suspended execution contexts. For example, when a new model-serving instance is launched, a previously initialized accelerator may act as a source of a snapshot for one or more newly allocated accelerators, thereby reducing the time required to make the new instance available to process inference requests. In some embodiments, this may improve responsiveness of an inference service, reduce queueing delay during scale-out events, and improve utilization of shared accelerator infrastructure.

[0199] In some embodiments, the copying operation may be coordinated by a runtime, scheduler, model-serving framework, container management component, accelerator management layer, or another control component. The control component may identify a source accelerator storing a usable snapshot, select one or more destination accelerators, determine a transfer order or topology, and initiate one or more copy operations over NVLink or another inter-accelerator communication path. In some embodiments, integrity checks, version checks, access control checks, or consistency checks may be performed before a copied snapshot is accepted for execution on a destination accelerator. The system may further verify that the copied snapshot corresponds to a correct model version, tenant context, configuration state, or execution environment.

[0200] In some embodiments, copying a snapshot between accelerators may avoid repeated deserialization, repeated decompression, repeated compilation, repeated host-memory staging, or repeated host-to-device transfer operations that would otherwise be performed independently for each accelerator. In some embodiments, a snapshot stored in device memory may be preserved in a form that is already arranged for execution or near-execution, such that transferring the snapshot to another accelerator reduces initialization overhead beyond simple file-loading savings. In other embodiments, a copied snapshot may be followed by one or more fix-up operations, including address adjustment, buffer remapping, context rebinding, partition-specific modification, or devicespecific initialization.

[0201] In some embodiments, the system may use peer-to-peer copying selectively for only a subset of model artifacts. For example, immutable weights or commonly reused runtime structures may be copied between accelerators, while device-specific state, security-sensitive state, or tenantspecific state may be separately generated, validated, or loaded at the destination accelerator. In some embodiments, this selective approach may preserve correctness, isolation, or security while still reducing startup latency. The disclosed techniques are therefore not limited to copying an entirePATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) model image and may be applied to any suitable subset of model state whose reuse across accelerators is permitted.

[0202] In some embodiments, use of NVLink or an equivalent accelerator-to-accelerator interconnect for snapshot copying may be combined with multi-tenant isolation controls, authorization checks, or communication mediation. For example, a control path may verify that a source accelerator and a destination accelerator belong to an authorized allocation group before permitting a snapshot copy. In this manner, startup latency reduction techniques may be combined with communication isolation techniques in shared accelerator environments.

[0203] Referring to Fig. 10, a flowchart is shown of an example method for reducing model load time in a multi-accelerator computing environment. In some embodiments, the method of Fig. 10 may be performed by a computing node, such as the GPU node 500 of Fig. 5, having a plurality of accelerators, such as GPUs 520, 522, and 524, and may be coordinated by a node-level control component, such as the node agent 510. In some embodiments, the method of Fig. 10 may be used in connection with startup reduction techniques of the type described with reference to Fig. 6, including restoration of model-related state for a standby inference container 502 so as to produce a warm or ready inference container 504. In some embodiments, the workload initialized using the method of Fig. 10 may comprise a model-serving workload distributed across multiple accelerators.

[0204] At operation 1002, the method may include loading a model snapshot into a memory of a first accelerator of a plurality of accelerators. In some embodiments, the model snapshot may be obtained from a snapshot store 512, from host memory, from local storage, from a distributed storage service, or from another data source accessible to the computing node. The model snapshot may comprise a stored representation of model state sufficient to initialize execution of a model and may include model weights, parameters, tensor data, runtime artifacts, cached compilation products, metadata, execution graphs, memory images, or combinations thereof. In some embodiments, the at least a portion of the model snapshot loaded at operation 1002 may comprise model weights. In some embodiments, operation 1002 may correspond to initially restoring GPU-resident state into one accelerator before that state is propagated to one or more additional accelerators, thereby avoiding repeated independent loading of the same snapshot content into each accelerator from host-side resources.

[0205] At operation 1004, the method may include identifying, by the computing node, one or more additional accelerators of the plurality of accelerators for execution of a workload associated with the model snapshot. In some embodiments, identifying the one or more additional accelerators may be performed by the node agent 510, a scheduler 514, a container runtime, a model-serving framework, or another control component having visibility into accelerator allocation and workloadPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) placement. For example, the computing node may determine that a selected model-serving workload is to be executed across GPUs 520, 522, and 524 of the GPU node 500, and may identify one of the GPUs as a source accelerator into which the model snapshot is initially loaded and one or more other GPUs as destination accelerators that are to receive copied snapshot content. In some embodiments, identification of the additional accelerators may be based on accelerator topology, available memory capacity, workload placement, execution configuration, tenancy constraints, service-level objectives, or another runtime condition.

[0206] At operation 1006, the method may include copying at least a portion of the model snapshot from the first accelerator to the one or more additional accelerators over an inter-accelerator communication path. In some embodiments, the inter-accelerator communication path may comprise NVLink. In other embodiments, the inter-accelerator communication path may comprise another peer-to-peer accelerator communication path, switched accelerator fabric, coherent accelerator interconnect, direct device-memory transfer path, or another communication fabric permitting accelerator-to-accelerator transfer. In some embodiments, the copied at least a portion of the model snapshot may comprise model weights, while in other embodiments the copied content may additionally or alternatively include tensor groups, partitions, runtime buffers, executable kernels, metadata, key-value caches, or other model-related state. In some embodiments, copying operation 1006 may comprise copying the at least a portion of the model snapshot from the first accelerator to the one or more additional accelerators in parallel. For example, a source accelerator may transmit corresponding portions of the model snapshot to multiple destination accelerators concurrently so as to reduce overall startup latency. In some embodiments, copying operation 1006 may comprise copying the at least a portion of the model snapshot according to a hierarchical distribution pattern across the plurality of accelerators. For example, the first accelerator may copy snapshot data to a second accelerator and a third accelerator, and the second accelerator may further forward at least a portion of the snapshot data to one or more additional accelerators, such that distribution occurs according to a fan-out, tree-based, pipelined, ring-based, mesh-based, or other multi-hop arrangement. In some embodiments, use of accelerator-to-accelerator copying at operation 1006 may reduce repeated host-to-device transfer operations and may take advantage of higher-bandwidth interconnect paths relative to independently loading the full snapshot from host-side resources into each accelerator.

[0207] In some embodiments, operation 1006 may be coordinated using host-side runtime components of the type described with reference to Figs. 8 and 9. For example, where a workload process issues a communication request associated with use of inter-accelerator communication resources, the request may be mediated through a communication control path and evaluated inPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) accordance with an isolation policy before the requested communication operation is permitted to proceed. In such embodiments, copying of the model snapshot between accelerators may be performed using an authorized inter-accelerator path so that startup-latency reduction may be combined with tenant isolation in a shared multi-accelerator environment. In some embodiments, the inter-accelerator communication used for snapshot copying may therefore correspond to authorized communication usage of the type described with reference to operations 908-914 of Fig. 9.

[0208] At operation 1008, the method may include initializing the workload on the plurality of accelerators based at least in part on the copied at least a portion of the model snapshot. In some embodiments, initializing the workload may comprise associating copied model state with a standby inference container 502 so as to produce a warm inference container 504 or another ready-to-execute instance, as described with reference to Fig. 5. In some embodiments, initialization may include reassociating CPU-side state and GPU-side state, performing one or more fix-up operations, establishing runtime bindings, activating communication resources, and preparing the workload for inference execution across the plurality of accelerators. In some embodiments, because at least a portion of the model snapshot has already been loaded into one accelerator and copied to one or more additional accelerators, the workload may be initialized across multiple accelerators without independently loading the same snapshot content into each accelerator from host-side storage or host memory. This may reduce model load time and startup latency for distributed inference or other multi-accelerator workloads.

[0209] In some embodiments, the disclosed architecture and methods provide a specific improvement in computer functionality and do not merely recite the use of a generic computer as a tool to perform an abstract objective. The disclosed subject matter is directed to a technological solution for a technological problem arising in GPU-based inference systems, namely, the delay, resource disruption, and repeated machine-level initialization associated with bringing model-serving execution environments into service on demand. As described herein, this problem arises from concrete computer operations including container launch, runtime establishment, framework initialization, host-memory preparation, pinned-memory registration, GPU-memory allocation, host-to-device transfer of model-related state, restoration of device-resident objects, and re-association of execution metadata across CPU-side and GPU-side contexts. In some embodiments, the disclosed techniques address these problems by modifying how the computer system itself prepares, preserves, restores, and activates execution state.

[0210] In some embodiments, the disclosed subject matter is implemented through a particular machine architecture and a particular sequence of machine operations that change the normal operation of the underlying computer platform. For example, the system may establish a standbyPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) inference container instance before request arrival, capture or associate snapshot state after at least part of an initialization sequence, preserve CPU-resident state and GPU-related restoration state, and later perform request-driven restoration that loads or re-associates the preserved state without repeating a full startup sequence from an uninitialized condition. This is not merely a result-oriented statement that a request should be handled more quickly. Rather, the disclosed subject matter specifies how the computer achieves that result by altering when and where initialization work is performed, how execution state is represented and stored, how preserved state is transferred and restored, and how an inference instance is transitioned from a standby condition to an active serving condition.

[0211] In some embodiments, the disclosed techniques improve the operation of the computer by reducing or avoiding repeated execution of machine-level startup tasks that would otherwise be performed after a request arrives. For example, instead of repeatedly performing full container bring-up, library binding, framework initialization, model-loading operations, and GPU-state establishment on the request path, the disclosed system may perform selected preparation operations in advance and then restore preserved execution state using node-level coordination when demand materializes. This changes the operation of the computer itself by reducing request-path computation, reducing repeated memory initialization activity, reducing repeated host-to-device data transfer activity, reducing repeated GPU object creation, and reducing the amount of runtime establishment needed after request receipt. In some embodiments, these changes yield concrete improvements in the functioning of the computer system, including reduced cold-start latency, improved response time, improved GPU-resource utilization, improved memory-management behavior, and improved throughput of the inference-serving platform.

[0212] In some embodiments, the disclosed subject matter also improves computer functionality at the level of resource orchestration and device-state management. For example, referring to Fig. 5, a node agent 510 may manage GPU-memory allocation, coordinate restoration of preserved state into GPU memory, control transfers from host memory or storage to one or more GPUs 520, 522, 524, and provide restored GPU objects or memory associations to an inference container instance. Such operations are not capable of performance as a mental process and are not merely organizational or administrative in nature. Instead, they are rooted in the mechanics of accelerator-backed computing infrastructure and are directed to a specific improvement in how the machine manages execution environments spanning container runtime state, host-memory state, and GPU-resident state. In some embodiments, the disclosed architecture therefore improves the functioning of the GPU node 500 itself by enabling the node to restore and activate model-serving contexts more efficiently than systems that rebuild those contexts from scratch after request arrival.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0213] In some embodiments, the disclosed subject matter is also not merely a generalized instruction to apply routine computer functions. The disclosed techniques rely on a non-conventional arrangement in which an inference container standby 502, an inference container warm 504, a node agent 510, a snapshot store 512, and one or more GPUs 520, 522, 524 cooperate to divide startup into a first phase and a second phase, preserve execution state between those phases, and restore that execution state in a manner that reduces the computational burden on the request path. This arrangement changes the state of the computer system over time and enables the system to maintain execution environments in intermediate readiness conditions that are neither fully absent nor fully active. In some embodiments, maintaining and transitioning among these intermediate readiness conditions provides a technical mechanism for improving startup behavior in large-model and GPU-backed inference systems.

[0214] The disclosed architecture and methods therefore provide a practical application implemented through specific machine components and specific state -transition operations, rather than an abstract idea divorced from technological implementation. The disclosed subject matter is tied to the concrete functioning of containerized inference environments, host-memory structures, snapshot representations, restoration metadata, GPU-memory contents, and request-driven activation logic operating within a GPU-based computing platform. Accordingly, the disclosed techniques may be understood as directed to a concrete improvement in computer technology itself, including the way a computer system stores execution state, stages initialization work, restores accelerator-backed runtime contexts, and transitions model-serving infrastructure into an operational state for inference execution.

[0215] The methods and systems described herein may be deployed in part or in whole through a machine having a computer, computing device, processor, circuit, and / or server that executes computer readable instructions, program codes, instructions, and / or includes hardware configured to functionally execute one or more operations of the methods and systems disclosed herein. The terms computer, computing device, processor, circuit, and / or server, as utilized herein, should be understood broadly.

[0216] Any one or more of the terms computer, computing device, processor, circuit, and / or server include a computer of any type, capable to access instructions stored in communication thereto such as upon a non-transient computer readable medium, whereupon the computer performs operations of systems or methods described herein upon executing the instructions. In certain embodiments, such instructions themselves comprise a computer, computing device, processor, circuit, and / or server. Additionally or alternatively, a computer, computing device, processor, circuit, and / or server may be a separate hardware device, one or more computing resources distributed across hardware devices,PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) and / or may include such aspects as logical circuits, embedded circuits, sensors, actuators, input and / or output devices, network and / or communication resources, memory resources of any type, processing resources of any type, and / or hardware devices configured to be responsive to determined conditions to functionally execute one or more operations of systems and methods herein.

[0217] Network and / or communication resources include, without limitation, local area network, wide area network, wireless, internet, or any other known communication resources and protocols. Example and non-limiting hardware, computers, computing devices, processors, circuits, and / or servers include, without limitation, a general purpose computer, a server, an embedded computer, a mobile device, a virtual machine, and / or an emulated version of one or more of these. Example and non-limiting hardware, computers, computing devices, processors, circuits, and / or servers may be physical, logical, or virtual. A computer, computing device, processor, circuit, and / or server may be: a distributed resource included as an aspect of several devices; and / or included as an interoperable set of resources to perform described functions of the computer, computing device, processor, circuit, and / or server, such that the distributed resources function together to perform the operations of the computer, computing device, processor, circuit, and / or server. In certain embodiments, each computer, computing device, processor, circuit, and / or server may be on separate hardware, and / or one or more hardware devices may include aspects of more than one computer, computing device, processor, circuit, and / or server, for example as separately executable instructions stored on the hardware device, and / or as logically partitioned aspects of a set of executable instructions, with some aspects of the hardware device comprising a part of a first computer, computing device, processor, circuit, and / or server, and some aspects of the hardware device comprising a part of a second computer, computing device, processor, circuit, and / or server.

[0218] A computer, computing device, processor, circuit, and / or server may be part of a server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platform. A processor may be any kind of computational or processing device capable of executing program instructions, codes, binary instructions and the like. The processor may be or include a signal processor, digital processor, embedded processor, microprocessor or any variant such as a co-processor (math co-processor, graphic co-processor, communication co-processor and the like) and the like that may directly or indirectly facilitate execution of program code or program instructions stored thereon. In addition, the processor may enable execution of multiple programs, threads, and codes. The threads may be executed simultaneously to enhance the performance of the processor and to facilitate simultaneous operations of the application. By way of implementation, methods, program codes, program instructions and the like described herein may be implemented in one or more threads. The thread may spawn other threads that may have assigned prioritiesPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) associated with them; the processor may execute these threads based on priority or any other order based on instructions provided in the program code. The processor may include memory that stores methods, codes, instructions and programs as described herein and elsewhere. The processor may access a storage medium through an interface that may store methods, codes, and instructions as described herein and elsewhere. The storage medium associated with the processor for storing methods, programs, codes, program instructions or other type of instructions capable of being executed by the computing or processing device may include but may not be limited to one or more of a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache and the like.

[0219] A processor may include one or more cores that may enhance speed and performance of a multiprocessor. In embodiments, the process may be a dual core processor, quad core processors, other chip-level multiprocessor and the like that combine two or more independent cores (called a die).

[0220] The methods and systems described herein may be deployed in part or in whole through a machine that executes computer readable instructions on a server, client, firewall, gateway, hub, router, or other such computer and / or networking hardware. The computer readable instructions may be associated with a server that may include a file server, print server, domain server, internet server, intranet server and other variants such as secondary server, host server, distributed server and the like. The server may include one or more of memories, processors, computer readable transitory and / or non-transitory media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other servers, clients, machines, and devices through a wired or a wireless medium, and the like. The methods, programs, or codes as described herein and elsewhere may be executed by the server. In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the server.

[0221] The server may provide an interface to other devices including, without limitation, clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers, and the like. Additionally, this coupling and / or connection may facilitate remote execution of instructions across the network. The networking of some or all of these devices may facilitate parallel processing of program code, instructions, and / or programs at one or more locations without deviating from the scope of the disclosure. In addition, all the devices attached to the server through an interface may include at least one storage medium capable of storing methods, program code, instructions, and / or programs. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for methods, program code, instructions, and / or programs.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0222] The methods, program code, instructions, and / or programs may be associated with a client that may include a file client, print client, domain client, internet client, intranet client and other variants such as secondary client, host client, distributed client and the like. The client may include one or more of memories, processors, computer readable transitory and / or non-transitory media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other clients, servers, machines, and devices through a wired or a wireless medium, and the like. The methods, program code, instructions, and / or programs as described herein and elsewhere may be executed by the client. In addition, other devices utilized for execution of methods as described in this application may be considered as a part of the infrastructure associated with the client.

[0223] The client may provide an interface to other devices including, without limitation, servers, other clients, printers, database servers, print servers, file servers, communication servers, distributed servers, and the like. Additionally, this coupling and / or connection may facilitate remote execution of methods, program code, instructions, and / or programs across the network. The networking of some or all of these devices may facilitate parallel processing of methods, program code, instructions, and / or programs at one or more locations without deviating from the scope of the disclosure. In addition, all the devices attached to the client through an interface may include at least one storage medium capable of storing methods, program code, instructions, and / or programs. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for methods, program code, instructions, and / or programs.

[0224] The methods and systems described herein may be deployed in part or in whole through network infrastructures. The network infrastructure may include elements such as computing devices, servers, routers, hubs, firewalls, clients, personal computers, communication devices, routing devices and other active and passive devices, modules, and / or components as known in the art. The computing and / or non-computing device(s) associated with the network infrastructure may include, apart from other components, a storage medium such as flash memory, buffer, stack, RAM, ROM and the like. The methods, program code, instructions, and / or programs described herein and elsewhere may be executed by one or more of the network infrastructural elements.

[0225] The methods, program code, instructions, and / or programs described herein and elsewhere may be implemented on a cellular network having multiple cells. The cellular network may either be frequency division multiple access (FDMA) network or code division multiple access (CDMA) network. The cellular network may include mobile devices, cell sites, base stations, repeaters, antennas, towers, and the like.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0226] The methods, program code, instructions, and / or programs described herein and elsewhere may be implemented on or through mobile devices. The mobile devices may include navigation devices, cell phones, mobile phones, mobile personal digital assistants, laptops, palmtops, netbooks, pagers, electronic books readers, music players, and the like. These mobile devices may include, apart from other components, a storage medium such as a flash memory, buffer, RAM, ROM and one or more computing devices. The computing devices associated with mobile devices may be enabled to execute methods, program code, instructions, and / or programs stored thereon.Alternatively, the mobile devices may be configured to execute instructions in collaboration with other devices. The mobile devices may communicate with base stations interfaced with servers and configured to execute methods, program code, instructions, and / or programs. The mobile devices may communicate on a peer to peer network, mesh network, or other communications network. The methods, program code, instructions, and / or programs may be stored on the storage medium associated with the server and executed by a computing device embedded within the server. The base station may include a computing device and a storage medium. The storage device may store methods, program code, instructions, and / or programs executed by the computing devices associated with the base station.

[0227] The methods, program code, instructions, and / or programs may be stored and / or accessed on machine readable transitory and / or non-transitory media that may include: computer components, devices, and recording media that retain digital data used for computing for some interval of time; semiconductor storage known as random access memory (RAM); mass storage typically for more permanent storage, such as optical discs, forms of magnetic storage like hard disks, tapes, drums, cards and other types; processor registers, cache memory, volatile memory, non-volatile memory; optical storage such as CD, DVD; removable media such as flash memory (e.g., USB sticks or keys), floppy disks, magnetic tape, paper tape, punch cards, standalone RAM disks, Zip drives, removable mass storage, off-line, and the like; other computer memory such as dynamic memory, static memory, read / write storage, mutable storage, read only, random access, sequential access, location addressable, file addressable, content addressable, network attached storage, storage area network, bar codes, magnetic ink, and the like.

[0228] Certain operations described herein include interpreting, receiving, and / or determining one or more values, parameters, inputs, data, or other information. Operations including interpreting, receiving, and / or determining any value parameter, input, data, and / or other information include, without limitation: receiving data via a user input: receiving data over a network of any type; reading a data value from a memory location in communication with the receiving device; utilizing a default value as a received data value; estimating, calculating, or deriving a data value based on otherPATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) information available to the receiving device; and / or updating any of these in response to a later received data value. In certain embodiments, a data value may be received by a first operation, and later updated by a second operation, as part of the receiving a data value. For example, when communications are down, intermittent, or interrupted, a first operation to interpret, receive, and / or determine a data value may be performed, and when communications are restored an updated operation to interpret, receive, and / or determine the data value may be performed.

[0229] Certain logical groupings of operations herein, for example methods or procedures of the current disclosure, are provided to illustrate aspects of the present disclosure. Operations described herein are schematically described and / or depicted, and operations may be combined, divided, reordered, added, or removed in a manner consistent with the disclosure herein. It is understood that the context of an operational description may require an ordering for one or more operations, and / or an order for one or more operations may be explicitly disclosed, but the order of operations should be understood broadly, where any equivalent grouping of operations to provide an equivalent outcome of operations is specifically contemplated herein. For example, if a value is used in one operational step, the determining of the value may be required before that operational step in certain contexts (e.g. where the time delay of data for an operation to achieve a certain effect is important), but may not be required before that operation step in other contexts (e.g. where usage of the value from a previous execution cycle of the operations would be sufficient for those purposes). Accordingly, in certain embodiments an order of operations and grouping of operations as described is explicitly contemplated herein, and in certain embodiments re-ordering, subdivision, and / or different grouping of operations is explicitly contemplated herein.

[0230] The methods and systems described herein may transform physical and / or or intangible items from one state to another. The methods and systems described herein may also transform data representing physical and / or intangible items from one state to another.

[0231] The elements described and depicted herein, including in flow charts, block diagrams, and / or operational descriptions, depict and / or describe specific example arrangements of elements for purposes of illustration. However, the depicted and / or described elements, the functions thereof, and / or arrangements of these, may be implemented on machines, such as through computer executable transitory and / or non-transitory media having a processor capable of executing program instructions stored thereon, and / or as logical circuits or hardware arrangements. Example arrangements of programming instructions include at least: monolithic structure of instructions; standalone modules of instructions for elements or portions thereof; and / or as modules of instructions that employ external routines, code, services, and so forth; and / or any combination of these, and all such implementations are contemplated to be within the scope of embodiments of thePATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) present disclosure Examples of such machines include, without limitation, personal digital assistants, laptops, personal computers, mobile phones, other handheld computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, electronic books, gadgets, electronic devices, devices having artificial intelligence, computing devices, networking equipment, servers, routers and the like. Furthermore, the elements described and / or depicted herein, and / or any other logical components, may be implemented on a machine capable of executing program instructions. Thus, while the foregoing flow charts, block diagrams, and / or operational descriptions set forth functional aspects of the disclosed systems, any arrangement of program instructions implementing these functional aspects are contemplated herein. Similarly, it will be appreciated that the various steps identified and described above may be varied, and that the order of steps may be adapted to particular applications of the techniques disclosed herein.Additionally, any steps or operations may be divided and / or combined in any manner providing similar functionality to the described operations. All such variations and modifications are contemplated in the present disclosure. The methods and / or processes described above, and steps thereof, may be implemented in hardware, program code, instructions, and / or programs or any combination of hardware and methods, program code, instructions, and / or programs suitable for a particular application. Example hardware includes a dedicated computing device or specific computing device, a particular aspect or component of a specific computing device, and / or an arrangement of hardware components and / or logical circuits to perform one or more of the operations of a method and / or system. The processes may be implemented in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable device, along with internal and / or external memory. The processes may also, or instead, be embodied in an application specific integrated circuit, a programmable gate array, programmable array logic, or any other device or combination of devices that may be configured to process electronic signals. It will further be appreciated that one or more of the processes may be realized as a computer executable code capable of being executed on a machine readable medium.

[0232] The computer executable code may be created using a structured programming language such as C, an object oriented programming language such as C++, or any other high-level or low-level programming language (including assembly languages, hardware description languages, and database programming languages and technologies) that may be stored, compiled or interpreted to run on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and computer readable instructions, or any other machine capable of executing program instructions.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO)

[0233] Thus, in one aspect, each method described above and combinations thereof may be embodied in computer executable code that, when executing on one or more computing devices, performs the steps thereof. In another aspect, the methods may be embodied in systems that perform the steps thereof, and may be distributed across devices in a number of ways, or all of the functionality may be integrated into a dedicated, standalone device or other hardware. In another aspect, the means for performing the steps associated with the processes described above may include any of the hardware and / or computer-readable instructions described above. All such permutations and combinations are contemplated in embodiments of the present disclosure.

[0234] While the disclosure has been disclosed in connection with the preferred embodiments shown and described in detail, various modifications and improvements thereon will become readily apparent to those skilled in the art. Accordingly, the spirit and scope of the present disclosure is not to be limited by the foregoing examples, but is to be understood in the broadest sense allowable by law.

Claims

PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) CLAIMSWhat is claimed is:

1. A method for reducing startup latency in a workload executed using at least one graphics processing unit (GPU), comprising:preparing, before a request is received, a standby instance with a runtime environment and associating the standby instance with snapshot data including GPU state;restoring, after the request is received, the GPU state from the snapshot data into GPU memory and associating the GPU state with the standby instance to make the instance ready to execute; andexecuting the workload using the ready to execute instance.

2. The method of claim 1, wherein preparing the standby instance further comprises pre-positioning the snapshot data in host memory.

3. The method of claim 1, wherein restoring the GPU state further comprises transferring the GPU state from host memory to GPU memory using parallel data loading across multiple GPUs.

4. The method of claim 1, wherein the snapshot data further comprises CPU state.

5. The method of claim 1, wherein the standby instance is associated with a container runtime or virtual machine environment.

6. The method of claim 1, wherein the method further comprises restoring CPU state from the snapshot data into the runtime environment.

7. The method of claim 1, wherein the standby instance is maintained in a standby state without occupying GPU execution resources.

8. The method of claim 1, wherein multiple standby instances are created from a common snapshot source.

9. The method of claim 1 , wherein the snapshot data includes restoration metadata for associating CPU state and GPU state during restoration.

10. The method of claim 1, wherein the method further comprises pre-warming the snapshot data in accessible storage before the request is received.

11. The method of claim 1 , wherein the method further comprises activating the standby instance by transitioning the standby instance to a ready state for inference serving.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) 12. The method of claim 1, wherein the method further comprises restoring GPU-resident state across multiple GPUs for a multi-GPU inference instance.

13. A computer-implemented method for reducing activation latency of a model-serving workload on accelerator hardware, the method comprising:initializing, on a host processor, an execution environment for a model-serving instance through at least a portion of a startup sequence that includes framework initialization and runtime configuration;capturing, after completion of the at least a portion of the startup sequence but prior to serving any external request, a snapshot that preserves execution state of the model-serving instance, the snapshot comprising CPU-resident state and GPU-related state data and including restoration metadata that identifies associations between the CPU-resident state and the GPU-related state data;storing the snapshot in storage accessible to a GPU node and pre-positioning at least a portion of the snapshot in host memory of the GPU node;maintaining a standby instance of the model-serving instance in the execution environment without allocating GPU memory for the model-serving instance and without loading GPU-resident state for the model-serving instance;receiving a request to execute the model-serving workload;selecting, in response to the request, the standby instance associated with the model-serving workload;coordinating, by a node-level controller, restoration of preserved execution state by:restoring at least a portion of the CPU-resident state from the snapshot into the execution environment of the standby instance; andloading the GPU-related state data from the snapshot into GPU memory of one or more GPUs of the GPU node and associating the loaded GPU-related state data with the standby instance in accordance with the restoration metadata; andactivating the standby instance as a ready instance and executing the model-serving workload using the ready instance on the one or more GPUs.

14. The method of claim 13, wherein initializing the execution environment comprises launching a container runtime or virtual machine environment and loading one or more model-serving framework libraries.

15. The method of claim 13, wherein capturing the snapshot is performed after model parameters have been loaded into memory but before any inference request is processed by the model-serving instance.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) 16. The method of claim 13, wherein the snapshot is generated at a plurality of different initialization checkpoints, and the method further comprises selecting, for restoration, a snapshot corresponding to a desired readiness level.

17. The method of claim 13, wherein the GPU-related state data comprises one or more of model weights, executable kernels, tensor buffers, activation buffers, or key-value cache structures.

18. The method of claim 13, wherein pre-positioning the snapshot comprises loading at least a portion of the snapshot into host memory while another portion of the snapshot remains stored in persistent storage.

19. The method of claim 13, wherein maintaining the standby instance without allocating GPU memory permits the GPU node to reassign GPU execution resources to a different workload while the standby instance is maintained.

20. The method of claim 13, wherein selecting the standby instance is based at least in part on predictive logic indicating an anticipated likelihood of receiving the request.

21. The method of claim 13, wherein restoring the GPU-related state data comprises transferring the GPU-related state data into GPU memory using parallel data transfers to multiple GPUs.

22. The method of claim 13, wherein restoring the CPU-resident state and loading the GPU-related state data are performed concurrently to reduce activation latency.

23. The method of claim 13, wherein the node-level controller manages GPU memory allocation across a plurality of GPUs before loading the GPU-related state data.

24. The method of claim 13, wherein activating the standby instance comprises re-associating restored CPU-resident state and GPU-related state using the restoration metadata.

25. The method of claim 13, wherein executing the model-serving workload comprises performing inference using a large language model to generate output tokens responsive to a prompt included in the request.

26. The method of claim 13, wherein multiple standby instances are maintained from a common snapshot, and the method further comprises independently activating respective standby instances in response to respective requests.

27. The method of claim 13, wherein the method reduces request-path startup latency relative to a cold initialization by avoiding repeated framework initialization and model loading after receipt of the request.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) 28. A system for reducing activation latency of a model-serving workload on accelerator hardware, the system comprising:a GPU node comprising a host processor, host memory, and one or more graphics processing units (GPUs) each having GPU memory;a snapshot store configured to store a snapshot captured after an execution environment for a model-serving instance has completed at least a portion of a startup sequence, the snapshot comprising CPU-resident state, GPU-related state data, and restoration metadata associating the CPU-resident state with the GPU-related state data;an execution environment instantiated on the host processor and configured to maintain a standby instance of the model-serving instance without allocating GPU memory and without loading GPU-resident state for the standby instance;a gateway configured to receive a request to execute the model-serving workload; and a node-level controller in communication with the GPU node and the snapshot store, the node-level controller being configured to:(i) pre-position, prior to receipt of the request, at least a portion of the snapshot in the host memory;(ii) select, in response to the request, the standby instance associated with the model-serving workload;(iii) restore at least a portion of the CPU-resident state from the snapshot into the execution environment of the standby instance; and(iv) load the GPU-related state data from the snapshot into the GPU memory of the one or more GPUs and associate the loaded GPU-related state data with the standby instance in accordance with the restoration metadata;wherein the execution environment is further configured to activate the standby instance as a ready instance and execute the model-serving workload on the one or more GPUs using the restored CPU-resident state and the loaded GPU-related state data.

29. The system of claim 28, wherein the execution environment comprises a container runtime configured to load one or more model-serving framework libraries.

30. The system of claim 28, wherein the snapshot stored in the snapshot store is captured after model parameters have been loaded into memory but before any inference request is processed by the model-serving instance.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) 31. The system of claim 28, wherein the snapshot data includes GPU-related state data comprising one or more of model weights, executable kernels, tensor buffers, activation buffers, or key-value cache structures.

32. The system of claim 28, wherein the standby instance is maintained without allocating GPU memory, thereby permitting the GPU node to reassign GPU execution resources to a different workload while the standby instance is maintained.

33. The system of claim 28, further comprising a scheduler configured to select the standby instance based at least in part on predictive logic indicating an anticipated likelihood of receiving the request.

34. The system of claim 28, wherein the node -level controller is configured to load the GPU-related state data into GPU memory using parallel data transfers to a plurality of GPUs.

35. The system of claim 28, wherein the node -level controller is configured to restore CPU-resident state and load GPU-related state data concurrently to reduce activation latency.

36. The system of claim 28, wherein the node -level controller is configured to manage allocation of GPU memory across a plurality of GPUs prior to loading the GPU-related state data.

37. The system of claim 28, wherein activating the standby instance comprises re-associating restored CPU-resident state and restored GPU-related state data using the restoration metadata.

38. The system of claim 28, wherein execution of the model-serving workload comprises performing inference using a large language model to generate output tokens responsive to a prompt included in the request.

39. A system for reducing startup latency in a workload executed using at least one graphics processing unit (GPU), comprising:a snapshot store storing snapshot data including GPU state;a gateway configured to receive a request for the workload;a scheduler configured to coordinate startup of a standby instance for the workload;a node agent configured to, before the request is received, prepare the standby instance with a runtime environment and associate the standby instance with the snapshot data without loading the GPU state into GPU memory and, after the request is received, restore the GPU state from the snapshot data into GPU memory and associate the restored GPU state with the standby instance to make the standby instance ready to execute the workload; andat least one GPU having the GPU memory.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) 40. The system of claim 39, wherein the snapshot data further comprises CPU-resident state and restoration metadata usable to re-associate CPU-resident state with the restored GPU state after the request is received.

41. The system of claim 39, wherein the node agent is further configured to pre-position at least a portion of the snapshot data in host memory prior to receipt of the request to reduce GPU state restoration latency.

42. The system of claim 39, wherein the standby instance is maintained without allocating GPU execution resources, thereby permitting the at least one GPU to be reassigned to a different workload prior to receipt of the request.

43. The system of claim 39, wherein the node agent is configured to restore GPU state into GPU memory of a plurality of GPUs to support execution of the workload as a multi-GPU inference instance.

44. The system of claim 39, wherein the scheduler is further configured to initiate preparation of the standby instance based on predictive logic indicating an anticipated likelihood of receiving the request for the workload.

45. An apparatus for reducing startup latency in a workload executed using at least one graphics processing unit (GPU), comprising:a processor configured to prepare, before a request is received, a standby instance with a runtime environment and associate the standby instance with snapshot data including GPU state, without loading the GPU state into GPU memory;at least one GPU configured to receive, after the request is received, the GPU state restored from the snapshot data into GPU memory and associate the GPU state with the standby instance to make the instance ready to execute; andwherein the processor is further configured to execute the workload using the ready to execute instance.

46. A method for providing tenant isolation in a multi-tenant, multi-accelerator computing environment, the method comprising:executing, in a guest user space, by a workload process, a workload associated with a tenant and issuing, by the workload process, a communication request associated with use of one or more accelerators of the node;intercepting, in the guest user space, the communication request;PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) forwarding, through a guest kernel space to a host user space, the communication request for evaluation;evaluating, in the host user space, the communication request based at least in part on an isolation policy to determine whether the communication request is authorized;responsive to determining that the communication request is authorized, executing a communication operation using communication resources of the computing node;providing, in a host kernel space, kernel-level support for the communication operation; and isolating inter-accelerator communication usage and network communication usage across tenants while permitting execution of a distributed workload.

47. The method of claim 46, wherein the workload comprises a model-serving workload.

48. The method of claim 46, wherein intercepting the communication request comprises intercepting a communication-library call.

49. The method of claim 48, wherein intercepting the communication-library call is performed by a communication-call intercept library in the guest user space.

50. The method of claim 46, wherein forwarding the communication request is performed by a container runtime in the guest kernel space.

51. The method of claim 46, wherein evaluating the communication request is performed in the host user space by a call security check.

52. The method of claim 51, wherein evaluating the communication request comprises determining whether the communication request corresponds to an allowed tenant context.

53. The method of claim 51, wherein evaluating the communication request comprises determining whether the communication request corresponds to an allowed accelerator allocation.

54. The method of claim 51, wherein evaluating the communication request comprises determining whether the communication request corresponds to an allowed inter-accelerator path.

55. The method of claim 51, wherein evaluating the communication request comprises determining whether the communication request corresponds to an allowed remote communication path.

56. The method of claim 46, wherein executing the communication operation is performed by a communication library in the host user space.

57. The method of claim 46, wherein providing the kernel-level support comprises providing, by a host kernel, access to host-level resources used to carry out the communication operation.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) 58. The method of claim 46, wherein the inter-accelerator communication usage comprises NVLink communication usage.

59. An apparatus for providing tenant isolation in a multi-tenant, multi-accelerator computing environment, the apparatus comprising:one or more processors, one or more accelerators, and a memory storing instructions that, when executed by the one or more processors, cause the apparatus to implement a guest user space, a guest kernel space, a host user space, and a host kernel space;a workload process in the guest user space, the workload process configured to execute a workload associated with a tenant and to issue a communication request associated with use of the one or more accelerators;a communication control path extending from the guest user space through the guest kernel space to the host user space, the communication control path configured to intercept the communication request, forward the communication request for evaluation, and, responsive to authorization of the communication request, enable execution of a communication operation using communication resources of the apparatus;a policy enforcement component in the host user space, the policy enforcement component configured to evaluate the communication request based at least in part on an isolation policy; and a kernel component in the host kernel space, the kernel component configured to provide kernel-level support for the communication operation,wherein the apparatus is configured to isolate inter-accelerator communication usage and network communication usage across tenants while permitting execution of a distributed workload.

60. The apparatus of claim 59, wherein the workload comprises a model-serving workload.

61. The apparatus of claim 59, wherein the communication request comprises a communicationlibrary call.

62. The apparatus of claim 59, wherein the communication control path comprises a communicationcall intercept library in the guest user space configured to intercept the communication request.

63. The apparatus of claim 59, wherein the communication control path comprises a container runtime in the guest kernel space configured to forward the communication request for evaluation.

64. The apparatus of claim 59, wherein the policy enforcement component comprises a call security check in the host user space configured to evaluate the communication request.PATENT Attorney Docket No. 2377.0005 (INFX-OOOl-WO) 65. A method for reducing model load time in a multi-accelerator computing environment, the method comprising:loading, by a computing node, a model snapshot into a memory of a first accelerator of a plurality of accelerators;identifying, by the computing node, one or more additional accelerators of the plurality of accelerators for execution of a workload associated with the model snapshot;copying, by the computing node, at least a portion of the model snapshot from the first accelerator to the one or more additional accelerators over an inter-accelerator communication path; andinitializing, by the computing node, the workload on the plurality of accelerators based at least in part on the copied at least a portion of the model snapshot.

66. The method of claim 65, wherein copying the at least a portion of the model snapshot comprises copying the at least a portion of the model snapshot from the first accelerator to the one or more additional accelerators in parallel.

67. The method of claim 65, wherein copying the at least a portion of the model snapshot comprises copying the at least a portion of the model snapshot according to a hierarchical distribution pattern across the plurality of accelerators.

68. The method of claim 65, wherein the at least a portion of the model snapshot comprises model weights.

69. The method of claim 65, wherein the inter-accelerator communication path comprises NVLink.

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