Summary of Zack: Zero-overhead Llm Inference Acceleration Via Dimensionality Compression Of the Key-value Cache, by Zeyu Zhang et al.
ZACK: Zero-Overhead LLM Inference Acceleration via Dimensionality Compression of the Key-Value Cache
by Zeyu Zhang, Haiying Shen
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 to key-value dimensionality compression in large-language models is proposed, addressing memory constraints during inference. ZACK, a zero-overhead compression and decompression system, reduces attention computation time and can be combined with existing methods to further enhance KV compression. Adaptive compression is employed, tailoring compression rates across heads and layers based on their contributions to inference while maintaining an accuracy loss constraint. The self-attention kernel is also enhanced to balance uneven workloads caused by adaptive compression. Comprehensive experiments demonstrate significant reductions in KV size, time-to-first-token, and time-between-tokens while maintaining baseline accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models need help with memory constraints during inference. A new approach called ZACK helps reduce memory use without slowing down the model. It works by compressing important information in the model’s “key-value cache” (KVC). This allows the model to use less memory and process information faster. ZACK can be used alone or combined with other methods to make it even more effective. The results show that using ZACK can reduce memory use, processing time, and increase the model’s speed while keeping its accuracy high. |
Keywords
» Artificial intelligence » Attention » Inference » Self attention » Token
