Summary of Mnemosyne: Parallelization Strategies For Efficiently Serving Multi-million Context Length Llm Inference Requests Without Approximations, by Amey Agrawal et al.
Mnemosyne: Parallelization Strategies for Efficiently Serving Multi-Million Context Length LLM Inference Requests Without Approximations
by Amey Agrawal, Junda Chen, Íñigo Goiri, Ramachandran Ramjee, Chaojie Zhang, Alexey Tumanov, Esha Choukse
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 tackles a critical challenge in large language models (LLMs): efficiently serving inference requests for extremely long contexts. Existing training techniques are not designed to address the unique latency constraints and varying prefill and decode phases that arise when processing contexts of millions of tokens. The authors highlight the lack of effective solutions for long context inference, particularly those that allow batching requests to increase hardware utilization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a superpowerful language model that can understand very long texts. But what if you want it to quickly answer questions about these texts? That’s the problem this paper solves! It shows how existing techniques are not good enough for handling really long contexts, and proposes new solutions to make this possible. |
Keywords
» Artificial intelligence » Inference » Language model
