Together AI has released OSCAR (Offline Spectral Covariance-Aware Rotation), an INT2 KV cache quantization method for long-context LLM serving. Unlike prior rotation-based approaches that apply data-oblivious Hadamard transforms, OSCAR derives separate rotations for keys and values from attention-aware covariance structures estimated offline. At 2.28 bits per KV element, OSCAR reduces the BF16 accuracy gap to 3.78 points on Qwen3-4B-Thinking-2507 and 1.42 points on Qwen3-8B, while delivering approximately 8× KV memory reduction and up to 3× decode speedup at 100K context length.
The post Together AI Open-Sources OSCAR: An Attention-Aware 2-Bit KV Cache Quantization System for Long-Context LLM Serving appeared first on MarkTechPost.
