WUSH-KV: KV Cache Quantization with Data-Adaptive Transforms
WUSH-KV quantizes KV caches with data-adaptive transforms and matches or beats OSCAR at 2 bits.
WUSH-KV applies the WUSH data-aware transform, built from second-order statistics of both factors in a matrix product, to low-bit key-value cache quantization. Calibration data produces separate key and value transforms: the value transform is folded into weights and the key transform is applied after RoPE. With QuEST INT, the transform is argued to be near-optimal and yields the lowest end-to-end perplexity among tested transforms. Integrated into SGLang with OSCAR-style percentile-clipped affine quantization, 2-bit WUSH-KV matches or outperforms the OSCAR transform across evaluated models and tasks.
- Separate key and value transforms come from calibration data.
- Value transform folds into weights; key transform follows RoPE.
- With QuEST, WUSH-KV has the lowest tested perplexity.
- At 2 bits in SGLang it matches or beats OSCAR.
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KV cache memory and bandwidth costs grow with context length and batch size, which limits efficient long-context inference. To address this bottleneck, we introduce WUSH-KV for low-bit KV-cache quantization. It adapts WUSH, which constructs a data-aware transform from the second-order statistics of both factors in a matrix product to reduce quantization error. WUSH-KV uses calibration data to construct separate key and value transforms, with the value transform folded into the model weights and the key transform applied after RoPE. The transforms can be paired with clipped quantizers. For one such quantizer, QuEST INT, we show that, under mild assumptions, the WUSH transform is near-optimal. With this quantizer, WUSH-KV reduces layerwise reconstruction error and achieves the lowest end-to-end perplexity among other tested transforms. For end-to-end evaluation, we integrate WUSH-KV into SGLang using OSCAR-style percentile-clipped affine quantization. At 2-bit, WUSH-KV performs comparably to or outperforms the OSCAR transform across all evaluated models and downstream tasks.
Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/papers/2609.38121