Summary of Kv Cache Is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization, by Tianyi Zhang et al.
KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization
by Tianyi Zhang, Jonah Yi, Zhaozhuo Xu, Anshumali Shrivastava
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper proposes Coupled Quantization (CQ), a novel technique for compressing key-value caches of Large Language Models (LLMs) during inference. To improve throughput, batching multiple requests together is crucial, but this approach can lead to GPU memory usage and latency bottlenecks as batch size, context length, or model size increase. Existing quantization methods struggle at low bit widths. The authors observe that channels in key-value activation embeddings are inter-dependent, allowing for more efficient encoding. CQ couples these channels together to compress activations while preserving model quality. Experimental results show CQ outperforms or matches existing baselines, even when the KV cache is quantized down to 1-bit. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make big language models faster and more efficient. Right now, it takes a lot of memory to run these models because they need to store lots of information in their “key-value” memory. The authors have a new idea called Coupled Quantization that can help reduce this memory usage while keeping the model’s quality good. They found that different parts of the model are connected and can be compressed together, which helps make it more efficient. |
Keywords
» Artificial intelligence » Context length » Inference » Quantization
