Summary of Communication Compression For Tensor Parallel Llm Inference, by Jan Hansen-palmus et al.
Communication Compression for Tensor Parallel LLM Inference
by Jan Hansen-Palmus, Michael Truong Le, Oliver Hausdörfer, Alok Verma
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 novel approach is presented for reducing latency in Large Language Models (LLMs) deployed on multiple hardware accelerators through Tensor Parallelism. The strategy involves compressing inter-accelerator communication to accelerate inference. By leveraging fine-grained quantization techniques, the authors achieve a 3.5-4.5x compression of selected activations, resulting in up to 2x reduction in time-to-first-token (TTFT) without compromising model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are very smart computer programs that can understand and generate human-like text. They are usually run on special machines called accelerators, which make them faster. The problem is that these models take a long time to respond because they need to talk to each other through the accelerators. This paper finds a way to speed up this process by reducing the amount of information being shared between the accelerators. They do this by making some calculations smaller and more efficient, which makes the model work faster without losing any quality. |
Keywords
* Artificial intelligence * Inference * Quantization * Token
