RaBitQCache: Rotated Binary Quantization for KVCache in Long Context LLM Inference

• URL: http://arxiv.org/abs/2606.31519v1
• Date: Tue, 30 Jun 2026 11:32:14 GMT
• Title: RaBitQCache: Rotated Binary Quantization for KVCache in Long Context LLM Inference
• Abstract summary: Long-context Large Language Model inference is severely bottlenecked by the massive Key-Value cache.<n>We propose RaBitQCache, a novel sparse attention framework that utilizes randomized rotated binary quantization.<n>We show that RaBitQCache significantly accelerates inference and reduces memory I/O while preserving generation quality compared to state-of-the-art baselines.
• Score: 16.966373205482256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-context Large Language Model inference is severely bottlenecked by the massive Key-Value (KV) cache, yet existing sparse attention methods often suffer from static fixed-budget (Top-k) retrieval or rely on proxy scores that are computationally expensive and biased. To address these limitations, we propose RaBitQCache, a novel sparse attention framework that utilizes randomized rotated binary quantization and high-throughput binary-INT4 arithmetic to efficiently estimate attention weights. Our proxy score serves as an unbiased estimator with a proven error bound, enabling adaptive Top-p retrieval that dynamically adjusts the token budget based on actual attention sparsity. We further implement a hardware-aware system with asynchronous pipelining and lazy updates to mask overhead. Evaluations demonstrate that RaBitQCache significantly accelerates inference and reduces memory I/O while preserving generation quality compared to state-of-the-art baselines. Code is available at https://github.com/Sakuraaa0/RaBitQCache.git.

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