Multi-Instance GPU Allocation for Dynamic LLM Inference Loads
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Solution Overview
Problem
Existing methods for allocating computing resources, such as GPUs, to Large Language Models (LLMs) often result in overallocation, leading to underutilization, increased latency, and wasteful energy consumption, particularly in dynamic and varied workload environments.
Innovation Solution
Implementing a system that analyzes inference requests and utilizes a database of profiles to dynamically configure Multi-Instance GPUs (MIGs) based on workload demands, optimizing resource allocation by adjusting GPU instances in real-time to achieve optimal performance, efficiency, and energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If GPU resources are overallocated to LLMs, then more workloads can be processed, but resource utilization decreases and energy consumption increases
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring workload characteristics and adjusting GPU resource allocation in real-time. The system transitions from static overallocation to dynamic adaptation, where resource allocation changes based on actual workload demands, thereby maintaining high productivity while reducing energy waste from unused resources.
Solution Approach 2:
The system changes allocation parameters by adjusting the amount of GPU resources assigned to different workloads based on analyzed workload characteristics. By modifying allocation parameters dynamically rather than using fixed overallocation, the system achieves optimal balance between processing capacity and energy consumption.
2Productivity
If GPU resources are overallocated to LLMs, then more workloads can be processed, but resource utilization decreases
Solution Approach 1:
The system dynamically adjusts resource allocation based on real-time workload analysis, transitioning from static overallocation to adaptive allocation. This ensures that the quantity of GPU resources matched to actual workload needs, maximizing utilization while maintaining productivity.
Solution Approach 2:
The system enables workloads to effectively request and receive appropriate resources through automated analysis and allocation. The workload characteristics are analyzed to determine optimal resource allocation, allowing the system to self-regulate resource distribution without manual intervention, thereby improving both utilization and productivity.
3Device complexity
If static resource allocation is used, then configuration is simple, but latency increases in dynamic workload environments
Solution Approach 1:
The patent transforms static resource allocation into a dynamic system that adapts to changing workload conditions. By continuously analyzing workload characteristics and adjusting allocations in real-time, the system reduces inference latency caused by mismatched static allocations while managing complexity through automated decision-making processes.
Solution Approach 2:
The system implements feedback mechanisms by monitoring workload performance and characteristics, then using this information to adjust resource allocation. This closed-loop approach optimizes latency by allocating resources based on actual workload needs rather than static pre-configuration, managing complexity through systematic feedback-driven adjustments.
4Device complexity
If static resource allocation is used, then configuration is simple, but efficiency decreases in varied workload environments
Solution Approach 1:
The system replaces simple static configuration with dynamic allocation that adapts to varied workload environments. By analyzing workload characteristics and adjusting resources in real-time, the system achieves high processing efficiency across diverse workloads while managing complexity through automated adaptation rather than manual reconfiguration.
Solution Approach 2:
The patent creates a universal allocation system that handles multiple types of workloads efficiently through a single dynamic framework. The system analyzes different workload characteristics and applies appropriate allocation strategies automatically, providing multi-functional capability that improves efficiency across varied environments without requiring separate static configurations for each workload type.
Data Source
AI summary
An embodiment analyzes an inference request to determine a set of parameters of execution and analyzes a computing environment of a Large Language Model (LLM) to extract a set of parameters of environment. A MIG in the set of MIGs in the environment includes a set of slices of a corresponding GPU (set of MIG slices). A profile is selected from a profiles database using some of the parameters of execution and some of the parameters of environment. By sending a set of instructions to a controller associated with the MIG, the controller is caused to modify an amount of a computing resource available to a MIG slice in the set of MIG slices, the amount being computed according to a performance specification corresponding to the profile. The inference request is scheduled to execute using the modified amount of computing resource at the MIG slice.