Summary of Kvquant: Towards 10 Million Context Length Llm Inference with Kv Cache Quantization, by Coleman Hooper et al.
KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization
by Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Michael W. Mahoney, Yakun Sophia Shao, Kurt Keutzer, Amir Gholami
First submitted to arxiv on: 31 Jan 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 KVQuant is a novel approach to compressing key-value cache activations in large language models (LLMs) that enables efficient inference on devices with limited memory. The method employs four techniques: Per-Channel Key Quantization, Pre-RoPE Key Quantization, Non-Uniform KV Cache Quantization, and Per-Vector Dense-and-Sparse Quantization. By applying KVQuant to LLaMA, Llama-2, Llama-3, and Mistral models, researchers achieved <0.1 perplexity degradation with 3-bit quantization on Wikitext-2 and C4 datasets. This breakthrough enables serving large models like LLaMA-7B with a context length of up to 1 million on a single A100-80GB GPU or up to 10 million on an 8-GPU system, while achieving speedups of up to ~1.7x compared to baseline fp16 matrix-vector multiplications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers have developed a new way to make large language models more efficient. These models need lots of memory to work well, but this can be a problem when we want to use them on devices with limited memory. The new method, called KVQuant, helps fix this issue by compressing the data that these models use. This allows us to use larger models like LLaMA-7B on devices with more memory, and even on devices with less memory, but still get good results. The team tested their approach with several different models and showed it works well, making big language models more practical for everyday use. |
Keywords
* Artificial intelligence * Context length * Inference * Llama * Perplexity * Quantization
