Larger models and longer contexts make the KV cache a memory and bandwidth bottleneck. We introduce Block-GTQ, a framework that uses the two-dimensional frequency blocks of rotary position embeddings (RoPE) to allocate cached-key precision. A blockwise logit-error bound motivates a label-free query-key energy score, which we combine with codec-dependent rate penalties. Under equal-cost budgets and diminishing marginal gains, largest-marginal-gain allocation exactly minimizes the separable integer proxy. We instantiate Block-GTQ with TurboQuant-MSE (TQ-MSE), grouping equal-rate blocks to support attention over packed caches without materializing a decoded fp16 KV cache. Across ten model configurations, K-only diagnostics reduce layer-averaged logit mean absolute error by about 32-80% relative to uniform TQ-MSE, improving all 367 layers at both 2- and 3-bit average logical key rates. At K2V2 (2 average logical bits per K/V coordinate) on Llama-3.1-8B-Instruct, Block-GTQ raises the six-task needle-in-a-haystack (NIAH) average from 70.6 to 97.4 and LongBench-EN from 33.7 to 44.9; K2V2 gains also hold on the tested 32B and 70B models. On DeepSeek-R1-Distill-Qwen-7B at K3V2, without fp16 sink or recent-token buffers, it achieves 49.6/38.8% avg@8 on AIME 2024/2025 versus fp16's 54.2/37.9%, where uniform TQ-MSE scores zero on both. On Qwen2.5-3B-Instruct, packed K3V3 serving on one H800 reduces resident KV footprint by a factor of 3.24 and retains perplexity close to fp16 at 128K, while uniform TQ-MSE degrades severely.

翻译：暂无翻译