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KVmix: Gra...
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Image Credit: Arxiv

KVmix: Gradient-Based Layer Importance-Aware Mixed-Precision Quantization for KV Cache

  • A new method named KVmix is proposed for mixed-precision quantization of Key-Value (KV) Cache to address high memory demands in Large Language Models (LLMs) inference.
  • KVmix utilizes gradient-based importance analysis to allocate layer-specific bit-widths, prioritizing important layers while aggressively quantizing less critical ones.
  • It introduces a dynamic long-context optimization strategy to balance accuracy and efficiency by keeping full-precision KV pairs for recent pivotal tokens and compressing older ones.
  • KVmix achieves near-lossless inference performance on LLMs like Llama and Mistral with significant memory compression and speedup in inference throughput.

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