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Quality Recovery for Quantized KV Caches via Low-Rank Attention Adaptation

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

Low-bit key--value (KV) caches reduce the memory required for autoregressive decoding, but the resulting quality loss depends on the model and quantizer. We keep the quantizer fixed and distill the floating-cache model's behavior into low-rank Q/K/V projection updates while the student executes a physically packed incremental cache. Across three seeds, 4-bit affine-cache adapters recover $54.24\%\pm2.47\%$ of the held-out perplexity gap on TinyLlama-1.1B and $75.96\%\pm4.04\%$ on Gemma-4-12B. On the same frozen NF4 Llama-3.1-8B base, one validation-selected run per quantizer recovers $60.42\%$

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