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原文摘要

INT2 KV-cache quantization is attractive for long-context LLM serving, yet making it both accurate and practical remains challenging. Data-oblivious rotations such as Hadamard transforms flatten channel outliers, but still degrade sharply at INT2 because they ignore how downstream attention queries the cache. We propose OSCAR, an ultra-low-bit KV-cache quantization method that estimates attention-aware covariance targets offline and uses them to derive fixed per-layer rotations and clipping thresholds. By aligning the quantization basis with the second-moment structures consumed by attention, OSCAR preserves attention fidelity at 2-bit precision. Beyond theoretical analysis under a frozen-error surrogate, we build a serving-ready OSCAR system with fused Triton write and decode kernels that preserve paged KV-cache layouts and prefix caching, integrating into modern serving engines such as SGLang and vLLM. We evaluate OSCAR on reasoning and coding benchmarks with generation traces of up to 32K tokens across five tasks. On Qwen3-4B-Thinking-2507 and Qwen3-8B, OSCAR narrows the BF16 accuracy gap to 3.78 and 1.21 points, respectively, where Hadamard-only INT2 collapses, and matches BF16 within run-to-run variance on Qwen3-32B and GLM-4.7 (358B parameters). On RULER-NIAH up to 128K tokens, OSCAR substantially outperforms QuaRot-style INT2 across context lengths. System-wise, OSCAR compresses KV-cache memory by nearly $8\times$, boosts multi-request throughput by up to $7.8\times$ at large batch sizes within the same memory budget, and accelerates batch-size-1 decoding by up to $3.1\times$ over BF16 by cutting KV memory traffic.