Summary of Guide: a Global Unified Inference Engine For Deploying Large Language Models in Heterogeneous Environments, by Yanyu Chen and Ganhong Huang
GUIDE: A Global Unified Inference Engine for Deploying Large Language Models in Heterogeneous Environments
by Yanyu Chen, Ganhong Huang
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper tackles the challenge of efficiently deploying large language models (LLMs) in real-world scenarios. Current obstacles include hardware heterogeneity, inference framework limitations, and workload complexities, which lead to inefficiencies in memory utilization, latency, and throughput. The authors identify key performance bottlenecks through extensive experiments, revealing a vast optimization space shaped by the interplay of hardware, frameworks, and workload parameters. To address these issues, they design a framework called GUIDE that leverages dynamic modeling and simulation-based optimization. This framework achieves prediction errors between 9.9% and 42.3% for key metrics such as batch latency, TTFT, and decode throughput. By bridging the gap between theoretical performance and practical deployment, GUIDE empowers practitioners to make data-driven decisions and unlock the full potential of LLMs in heterogeneous environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem: making large language models work well in real-world situations. Right now, it’s hard because different devices have different abilities, the software can’t handle it, and there are lots of complicated things going on. This makes it slow and uses too much memory. The authors did lots of tests to figure out what’s going wrong and found that there are many places where they could make improvements. They created a new way to do this called GUIDE that uses special computer programs to find the best solutions. With GUIDE, people can use these models in different devices without it being too slow or using too much memory. |
Keywords
» Artificial intelligence » Inference » Optimization
