Reasoning models need special quantization handling because they show larger activation fluctuations early in generation—CodeQuant2 captures this with adaptive clustering and better calibration, achieving 2.85× speedup with minimal accuracy loss.
CodeQuant2 is a quantization technique that compresses large reasoning models to low precision while maintaining accuracy. It addresses unique challenges in reasoning models by detecting and handling unusual activation patterns in early decoding steps, using clustering to represent weights efficiently, and adapting compression levels per layer.