OSCAR RotationZoo - Offline Spectral Covariance-Aware Rotation for 2-bit KV Cache Quantization
OSCAR uses precomputed spectral covariance-aware rotation matrices to enable 2-bit KV cache quantization with minimal quality loss.
OSCAR applies offline-precomputed rotation matrices—derived from spectral covariance analysis—to reshape KV tensor distributions before 2-bit quantization, suppressing outliers and reducing rounding error. The rotation adds negligible inference overhead since it requires no runtime learning. GGUF downloads for Gemma-4-12B-it, Qwen3-32B, and Qwen3-4B-Thinking are available, with llama.cpp and sglang integrations and an arXiv paper.
OSCAR (Offline Spectral Covariance-Aware Rotation) is a KV Cache quantization technology optimized for inference memory in large language models (LLMs). Its core goal is to compress the Key-Value cache in the attention mechanism to 2-bit precision while preserving model output quality as much as possible.
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