Summary of Amusd: Asynchronous Multi-device Speculative Decoding For Llm Acceleration, by Bradley Mcdanel
AMUSD: Asynchronous Multi-Device Speculative Decoding for LLM Acceleration
by Bradley McDanel
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
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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 proposed AMUSD system revolutionizes large language model generation by decoupling the draft and verify phases, enabling both models to perform predictions asynchronously on separate devices. This novel approach builds upon recent work in speculative decoding, which accelerates token generation using a smaller, faster draft model. In this paper, the authors demonstrate an average 29% improvement over speculative decoding and up to 1.96speedup over conventional autoregressive decoding, while maintaining identical output quality across multiple datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting smarter! Researchers have found a way to make them work faster too. They did this by letting two different parts of the model do their jobs at the same time, instead of waiting for one part to finish before moving on to the next. This new approach is called AMUSD and it can make big language models work up to 96% faster than before! It’s a game-changer for all sorts of applications that use these models. |
Keywords
» Artificial intelligence » Autoregressive » Large language model » Token
