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SKVQ: Sliding-window Key and Value Cache Quantization for Large Language Models
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SKVQ: Sliding-window Key and Value Cache Quantization for Large Language Models
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Large language models (LLMs) can now handle longer sequences of tokens, enabling complex tasks like book understanding and generating lengthy novels. However, the key-value (KV) cache required for LLMs consumes substantial memory as context length increasing, becoming the bottleneck for deployment. In this paper, we present a strategy called SKVQ, which stands for sliding-window KV cache quantization, to address the issue of extremely low bitwidth KV cache quantization. To achieve this, SKVQ rearranges the channels of the KV cache in order to improve the similarity of channels in quantization groups, and applies clipped dynamic quantization at the group level. Additionally, SKVQ ensures that the most recent window tokens in the KV cache are preserved with high precision. This helps maintain the accuracy of a small but important portion of the KV cache.SKVQ achieves high compression ratios while maintaining accuracy. Our evaluation on LLMs demonstrates that SKVQ surpasses previous quantization approaches, allowing for quantization of the KV cache to 2-bit keys and 1.5-bit values with minimal loss of accuracy. With SKVQ, it is possible to process context lengths of up to 1M on an 80GB memory GPU for a 7b model and up to 7 times faster decoding.
Forward citations
Cited by 6 Pith papers
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Dual Dimensionality for Local and Global Attention
Distance-Adaptive Representation (DAR) keeps full KV dimensionality inside a local window and reduces it to 1/4 outside, matching full-dimensional baselines on pretraining (70M-410M) and 1B-scale fine-tuning while uni...
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KVServe: Service-Aware KV Cache Compression for Communication-Efficient Disaggregated LLM Serving
KVServe delivers up to 9.13x job completion time speedup and 32.8x time-to-first-token reduction by making KV cache compression service-aware and adaptive in disaggregated LLM serving.
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OScaR: The Occam's Razor for Extreme KV Cache Quantization in LLMs and Beyond
OScaR mitigates token norm imbalance via canalized rotation and omni-token scaling to enable near-lossless INT2 KV cache quantization with up to 3x decoding speedup and 5.3x memory reduction.
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WindowQuant: Mixed-Precision KV Cache Quantization based on Window-Level Similarity for VLMs Inference Optimization
WindowQuant performs window-adaptive mixed-precision KV cache quantization guided by similarity to the text prompt, with reordering to enable efficient inference in VLMs.
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Harmonia: Algorithm-Hardware Co-Design for Memory- and Compute-Efficient BFP-based LLM Inference
Harmonia runs LLM inference with all activations in block floating point (BFP) and a 4-bit KV cache, reporting 3.08x average speedup, 2.03x energy savings, and under 1% accuracy loss on LongBench.
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Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization
Quant VideoGen reduces KV cache memory by up to 7 times in autoregressive video diffusion models via semantic aware smoothing and progressive residual quantization, achieving better quality than baselines with under 4...
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