microsoft/VPTQ

Vector Post-Training Quantization (VPTQ) is a novel Post-Training Quantization method that leverages Vector Quantization to high accuracy on LLMs at an extremely low bit-width (<2-bit). VPTQ can compress 70B, even the 405B model, to 1-2 bits without retraining and maintain high accuracy.

• —Better Accuracy on 1-2 bits, (405B @ <2bit, 70B @ 2bit)
• —Lightweight Quantization Algorithm: only cost ~17 hours to quantize 405B Llama-3.1
• —Agile Quantization Inference: low decode overhead, best throughput, and TTFT

Github/Codes

Online Demo

Paper