Summary of Pipeinfer: Accelerating Llm Inference Using Asynchronous Pipelined Speculation, by Branden Butler et al.
PipeInfer: Accelerating LLM Inference using Asynchronous Pipelined Speculation
by Branden Butler, Sixing Yu, Arya Mazaheri, Ali Jannesari
First submitted to arxiv on: 16 Jul 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 PipeInfer technique aims to improve the performance of large language model (LLM) inference across computer clusters. Traditional acceleration methods reduce memory bandwidth bottlenecks but increase end-to-end latency, requiring high speculation acceptance rates. This can lead to reduced performance when combined with variable task acceptance rates and pipeline-parallel designs. To address these limitations, PipeInfer uses a pipelined speculative acceleration approach that reduces inter-token latency, improves system utilization for single-request scenarios, and enhances tolerance to low speculation acceptance rates and bandwidth interconnects. The technique achieves up to 2.15x improvement in generation speed over standard speculative inference by leveraging Continuous Asynchronous Speculation and Early Inference Cancellation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PipeInfer is a new way to make language models faster on computers. Right now, it takes a long time to do lots of things with big language models because they need to use all the computer’s power at once. This makes it hard for smaller tasks to get done quickly. PipeInfer fixes this by letting the computer do many small tasks at the same time, which makes everything run faster and better. |
Keywords
» Artificial intelligence » Inference » Large language model » Token
