MIG-Aware GPU Autoscaling for LLM Inference Memory Allocation
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Solution Overview
Problem
Existing methods for managing GPU resources in AI model services lead to overallocation, resulting in underutilization, increased latency, and unnecessary wear and tear, while failing to optimize resource allocation based on dynamic workload demands.
Innovation Solution
Implementing Multi-Instance GPU (MIG) aware autoscaling that dynamically allocates and reconfigures GPU resources based on workload requirements, using attention layer intermediate results as indicators for optimal memory allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If GPU resources are overallocated to handle inference requests, then service capacity is improved, but resource utilization deteriorates and latency increases
Solution Approach 1:
The system dynamically allocates GPU resources by monitoring workload demands in real-time and adjusting resource allocation accordingly. The autoscaling mechanism changes the degree of resource allocation dynamically based on current inference request patterns, transitioning from static overallocation to adaptive resource management that maintains both high service capacity and efficient utilization.
Solution Approach 2:
The system implements feedback loops that monitor GPU utilization metrics, inference latency, and workload patterns. This feedback information is used to adjust resource allocation decisions, creating a closed-loop control system that prevents both overallocation and underallocation by continuously adapting to actual system conditions and performance requirements.
2Device complexity
If GPU resources are statically allocated, then resource allocation simplicity is improved, but adaptability to dynamic workload demands deteriorates
Solution Approach 1:
The patent transforms static GPU resource allocation into a dynamic system that automatically adjusts resource distribution based on workload characteristics. The autoscaling mechanism introduces temporal variability to resource allocation, allowing the system to adapt to changing inference demands while maintaining manageable complexity through automated decision-making algorithms.
Solution Approach 2:
The system implements self-service resource allocation where the GPU resource management system automatically monitors its own utilization and adjusts allocation without external intervention. This self-managing approach maintains simplicity by eliminating manual configuration while achieving high adaptability through automated response to workload changes.
3Speed
If GPU resources are allocated without considering intermediate results memory needs, then allocation speed is improved, but manufacturing precision of resource allocation deteriorates
Solution Approach 1:
The system performs preliminary analysis of inference requests to estimate memory requirements for intermediate results before allocating GPU resources. By pre-calculating memory needs based on request characteristics and model architecture, the system prepares allocation parameters in advance, enabling both fast execution and precise resource matching without compromising allocation speed.
Solution Approach 2:
The patent replaces manual or rule-based resource allocation mechanisms with an automated system that uses attention layer intermediate results analysis to determine optimal memory allocation. This substitution introduces intelligent calculation that precisely matches resource allocation to actual computational needs while maintaining rapid allocation through algorithmic efficiency.
Data Source
AI summary
An embodiment analyzes an inference request to determine a set of parameters of execution corresponding to the inference request. For a Large Language Model (LLM), a first amount of a computing resource is computed, that amount of computing resource being estimated to be needed to produce a set of intermediate results while processing the inference request by executing the LLM using a set of multi-instance Graphical Processing Units (GPUs) (MIGs), a MIG in the set of MIGs comprising a set of slices of a corresponding GPU (set of MIG slices). A set of instructions is sent to a controller associated with the MIG, to cause the controller to modify a second amount of the computing resource available to a MIG slice in the set of MIG slices. The inference request is scheduled to execute using the first amount of computing resource at the MIG slice.