Summary of Etalon: Holistic Performance Evaluation Framework For Llm Inference Systems, by Amey Agrawal et al.
Etalon: Holistic Performance Evaluation Framework for LLM Inference Systems
by Amey Agrawal, Anmol Agarwal, Nitin Kedia, Jayashree Mohan, Souvik Kundu, Nipun Kwatra, Ramachandran Ramjee, Alexey Tumanov
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 A recent surge in optimizations for large language model (LLM) inference systems aims to reduce costs and improve user-facing performance. Current metrics, such as TTFT, TBT, Normalised Latency, and TPOT, assess latency and throughput but fail to capture the nuances of LLM inference. This paper identifies these pitfalls and proposes Etalon, a comprehensive evaluation framework that includes fluidity-index, a novel metric designed to reflect the intricacies of the LLM inference process. Etalon is used to evaluate existing open-source platforms and model-as-a-service offerings, highlighting their strengths and weaknesses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are powerful tools that can help with tasks like chat and translation. When we use them in real-time applications, it’s important to make sure they work well and don’t slow down the user experience. Right now, there are some problems with how we evaluate LLMs. This paper talks about those issues and proposes a new way to test LLMs that takes into account how they affect real-time user experiences. |
Keywords
* Artificial intelligence * Inference * Large language model * Translation
