MiniKV: Pushing the Limits of LLM Inference via 2-Bit Layer-Discriminative KV Cache

• URL: http://arxiv.org/abs/2411.18077v2
• Date: Thu, 28 Nov 2024 02:01:50 GMT
• Title: MiniKV: Pushing the Limits of LLM Inference via 2-Bit Layer-Discriminative KV Cache
• Authors: Akshat Sharma, Hangliang Ding, Jianping Li, Neel Dani, Minjia Zhang,
• Abstract summary: Mini KV is a KV cache optimization method that simultaneously preserves long context task accuracy while significantly reducing KV cache size. We show that Mini KV achieves 86% KV cache compression ratio while recovering over 98.5% of accuracy, outperforming state-of-the-art methods.
• Score: 17.58398289266989
• License:
• Abstract: How to efficiently serve LLMs in practice has become exceptionally challenging due to their prohibitive memory and computation requirements. In this study, we investigate optimizing the KV cache, whose memory footprint poses a critical bottleneck in LLM inference, especially when dealing with long context tasks. To tackle the challenge, we introduce MiniKV, a KV cache optimization method that simultaneously preserves long context task accuracy while significantly reducing KV cache size via a novel 2-bit layer-discriminative KV cache. More importantly, we develop specialized CUDA kernels to make MiniKV compatible with FlashAttention. Experiments on a wide range of long context tasks show that MiniKV effectively achieves 86% KV cache compression ratio while recovering over 98.5% of accuracy, outperforming state-of-the-art methods while achieving excellent measured system performance improvements.

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