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GUIDE: A Global Unified Inference Engine for Deploying Large Language Models in Heterogeneous Environments

Yanyu Chen, Ganhong Huang

TL;DR

GUIDE presents a holistic, simulation-based framework to optimize LLM inference in heterogeneous environments by integrating memory-aware modeling, dynamic parameter adjustment, and hybrid data/tensor parallelism. Through a Roofline-guided parallel simulator and an analyzer that dynamically tunes batch size and sequence length, GUIDE predicts performance and identifies near-optimal configurations under budget and hardware constraints. The approach addresses memory bottlenecks, latency variability, and multi-GPU scaling, validating predictions against real hardware with reasonable error ranges ($9.9\%$ to $42.3\%$). This framework enables practitioners to deploy large-scale models more efficiently and cost-effectively, bridging the gap between theoretical performance and practical deployment in diverse environments.

Abstract

Efficiently deploying large language models (LLMs) in real-world scenarios remains a critical challenge, primarily due to hardware heterogeneity, inference framework limitations, and workload complexities.Efficiently deploying large language models (LLMs) in real-world scenarios remains a critical challenge, primarily due to hardware heterogeneity, inference framework limitations, and workload complexities. These challenges often lead to inefficiencies in memory utilization, latency, and throughput, hindering the effective deployment of LLMs, especially for non-experts. Through extensive experiments, we identify key performance bottlenecks, including sudden drops in memory utilization, latency fluctuations with varying batch sizes, and inefficiencies in multi-GPU configurations. These insights reveal a vast optimization space shaped by the intricate interplay of hardware, frameworks, and workload parameters. This underscores the need for a systematic approach to optimize LLM inference, motivating the design of our framework, GUIDE. GUIDE leverages dynamic modeling and simulation-based optimization to address these issues, achieving prediction errors between 9.9% and 42.3% for key metrics such as batch latency, TTFT, and decode throughput. By effectively bridging the gap between theoretical performance and practical deployment, our framework empowers practitioners, particularly non-specialists, to make data-driven decisions and unlock the full potential of LLMs in heterogeneous environments cheaply.

GUIDE: A Global Unified Inference Engine for Deploying Large Language Models in Heterogeneous Environments

TL;DR

GUIDE presents a holistic, simulation-based framework to optimize LLM inference in heterogeneous environments by integrating memory-aware modeling, dynamic parameter adjustment, and hybrid data/tensor parallelism. Through a Roofline-guided parallel simulator and an analyzer that dynamically tunes batch size and sequence length, GUIDE predicts performance and identifies near-optimal configurations under budget and hardware constraints. The approach addresses memory bottlenecks, latency variability, and multi-GPU scaling, validating predictions against real hardware with reasonable error ranges ( to ). This framework enables practitioners to deploy large-scale models more efficiently and cost-effectively, bridging the gap between theoretical performance and practical deployment in diverse environments.

Abstract

Efficiently deploying large language models (LLMs) in real-world scenarios remains a critical challenge, primarily due to hardware heterogeneity, inference framework limitations, and workload complexities.Efficiently deploying large language models (LLMs) in real-world scenarios remains a critical challenge, primarily due to hardware heterogeneity, inference framework limitations, and workload complexities. These challenges often lead to inefficiencies in memory utilization, latency, and throughput, hindering the effective deployment of LLMs, especially for non-experts. Through extensive experiments, we identify key performance bottlenecks, including sudden drops in memory utilization, latency fluctuations with varying batch sizes, and inefficiencies in multi-GPU configurations. These insights reveal a vast optimization space shaped by the intricate interplay of hardware, frameworks, and workload parameters. This underscores the need for a systematic approach to optimize LLM inference, motivating the design of our framework, GUIDE. GUIDE leverages dynamic modeling and simulation-based optimization to address these issues, achieving prediction errors between 9.9% and 42.3% for key metrics such as batch latency, TTFT, and decode throughput. By effectively bridging the gap between theoretical performance and practical deployment, our framework empowers practitioners, particularly non-specialists, to make data-driven decisions and unlock the full potential of LLMs in heterogeneous environments cheaply.

Paper Structure

This paper contains 41 sections, 16 equations, 14 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background & Motivation
  3. Transformer Models
  4. LLM Inference Challenges
  5. LLM Inference Optimization
  6. Algorithmic Optimizations
  7. Inference Frameworks
  8. Deployment Strategies
  9. Experimental Insights
  10. Memory Utilization Drop-Offs
  11. Batch Size and Latency Divergence
  12. Performance Under Parallel Environments
  13. Dynamic Batch Size Optimization
  14. Motivation
  15. Framework
  16. ...and 26 more sections

Figures (14)

  • Figure 1: Workflow of the GUIDE system, which helps users input cost and requirements to generate an optimized LLM inference configuration.
  • Figure 2: Basic transformer architecture.
  • Figure 3: Logical-to-physical block mapping in vLLM.
  • Figure 4: The architecture of the DeepSpeed-FastGen backend, showing continuous batching and dynamic splitting in DeepSpeed-MII, and block KV-cache in DeepSpeed-Inference.
  • Figure 5: Memory utilization drop-offs.
  • ...and 9 more figures