Channel-Aware Mixed-Precision Quantization for Efficient Long-Context Inference

The key-value (KV) cache plays a vital role in accelerating autoregressive inference for large language models (LLMs). However, its linear memory growth with sequence length poses significant memory bottlenecks, especially in long-context scenarios. Quantization offers a promising solution for memory efficiency. While existing methods typically apply channel-wise quantization to the key cache and token-wise quantization to the value cache, they suffer from severe performance degradation under low-bit configurations. Our analysis reveals that quantization sensitivity varies across individual KV channels, presenting an opportunity for non-uniform bit allocation. Following this finding, we propose ChanMix, a mixed-precision quantization framework that supports channel-wise quantization on 2-bit setting with FP8 precision with a custom Triton kernel implementation. To improve low-bit quantization performance, we introduce a channel-aware bit reallocation strategy, which allocates bits across channel sensitivity. Through extensive evaluation, ChanMix demonstrates superior performance across the NIAH, RULER, and InfiniteBench benchmarks for the Llama, Mistral, and Qwen model families, achieving improvements of at least 5 absolute percentage points on RULER compared to all baseline methods. Additionally, ChanMix enables a 2.3× increase in batch size and supports a 1.5× longer context length during inference.