Summary of Performance Characterization Of Expert Router For Scalable Llm Inference, by Josef Pichlmeier et al.
Performance Characterization of Expert Router for Scalable LLM Inference
by Josef Pichlmeier, Philipp Ross, Andre Luckow
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Performance (cs.PF)
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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 proposes Expert Router, a scalable routing architecture that directs prompts to specialized expert models to optimize Large Language Models (LLMs) for diverse tasks. By combining optimized models through a routing mechanism, the system addresses challenges in deploying and serving LLMs at scale. The authors characterize multiple Expert Router configurations using LLama 3 models with quantized and non-quantized weights under concurrent users, revealing minimal latency overhead and stable performance outcomes. The findings highlight the potential of Expert Router for efficient and scalable LLM deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to make large language models work better together. Right now, these models are really good at doing lots of things, but they can be slow and take up a lot of computer power. This new system, called Expert Router, helps solve this problem by matching the right model with the job it needs to do. The researchers tested different versions of their system and found that it works well even when many people are using it at the same time. |
Keywords
» Artificial intelligence » Llama
