Your KV Cache Doesn’t Have a Bit Problem. It Has a Geometry Problem.

At identical 2-bit precision, one decision about which axis you quantize along swings a benchmark score from 2.88 to 63.53. Keys and values need opposite treatment — and the reason is in the attention equation, not the hardware. Take Llama-2-13B. Group its key-value cache by a quantization group size of 32 into two bits, while leaving everything else in place — same model, same bit budget, same group sizes, same benchmarks. Depending on one single element of an implementation decision, CoQA…

This article has been indexed from Unite.AI

Read the original article: