Summary of Any-precision Llm: Low-cost Deployment Of Multiple, Different-sized Llms, by Yeonhong Park et al.
Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs
by Yeonhong Park, Jake Hyun, SangLyul Cho, Bonggeun Sim, Jae W. Lee
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper introduces any-precision Large Language Models (LLMs), extending the concept of any-precision DNNs to LLMs. It proposes a lightweight method for quantizing LLMs using a post-training quantization framework, along with a specialized software engine for efficient serving. The solution overlays LLMs quantized to varying bit-widths into a memory footprint comparable to a single n-bit LLM. This reduces the costs of deploying multiple, different-sized LLMs while maintaining state-of-the-art model quality and inference throughput. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to use many different-sized Large Language Models (LLMs) without using up too much space or money. It does this by creating a special way to shrink these models down while keeping them working well, and making a tool to help make them work quickly. This can be very helpful for people who want to use many LLMs at the same time. |
Keywords
* Artificial intelligence * Inference * Precision * Quantization
