InnerQ: Hardware-Aware Tuning-Free Quantization of KV Cache for Large Language Models

arXiv:2602.23200v2 Announce Type: replace Abstract: When transformer-based language models are deployed for text generation, most of the inference time is spent in the decoding stage, where output tokens are generated sequentially. Reducing the hardware cost of each decoding step is therefore critical for efficient long-context generation. A major bottleneck is the key-value (KV) cache, whose size grows with sequence length and often dominates the model's memory footprint. Prior work has proposed quantization methods to compress the KV cache while minimizing its loss of precision. We present I

Source: arXiv cs.LG — read the full report at the original publisher.

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