SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Guangxuan Xiao*¹, Ji Lin*¹, Mickael Seznec², Hao Wu², Julien Demouth², Song Han¹
Massachusetts Institute of Technology¹, NVIDIA²
(* indicates equal contribution)


  • 2024-03-29

    We show SmoothQuant can enable W8A8 quantization for Llama-1/2, Falcon, Mistral, and Mixtral models with negligible loss.

  • 2024-01-17

    SmoothQuant is integrated into Microsoft's ONNX Runtime.

  • 2023-12-05

    SmoothQuant is integrate by NVIDIA TensorRT-LLM.


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Large language models (LLMs) show excellent performance but are compute- and memory-intensive. Quantization can reduce memory and accelerate inference. However, existing methods cannot maintain accuracy and hardware efficiency at the same time. We propose SmoothQuant, a training-free, accuracy-preserving, and general-purpose post-training quantization (PTQ) solution to enable 8-bit weight, 8-bit activation (W8A8) quantization for LLMs. Based on the fact that weights are easy to quantize while activations are not, SmoothQuant smooths the activation outliers by offline migrating the quantization difficulty from activations to weights with a mathematically equivalent transformation. SmoothQuant enables an INT8 quantization of both weights and activations for all the matrix multiplications in LLMs, including OPT, BLOOM, GLM, MT-NLG, and LLaMA families. We demonstrate up to 1.56x speedup and 2x memory reduction for LLMs with negligible loss in accuracy. SmoothQuant enables serving 530B LLM within a single node. Our work offers a turn-key solution that reduces hardware costs and democratizes LLMs.

  • SmoothQuant migrates part of the quantization difficulties from activation to weights, which smooths out the systematic outliers in activation, making both weights and activations easy to quantize.
  • SmoothQuant can achieve W8A8 quantization of LLMs (e.g., OPT-175B) without degrading performance.
  • SmoothQuant can achieve faster inference compared to FP16 when integrated into PyTorch, while previous work LLM.int8() does not lead to acceleration (usually slower).
  • We also integrate SmoothQuant into the state-of-the-art serving framework FasterTransformer, achieving faster inference speed using only half the GPU numbers compared to FP16 (1 instead of 2 for OPT-66B, 4 instead of 8 for OPT-175B).



 title =  {{S}mooth{Q}uant: Accurate and Efficient Post-Training Quantization for Large Language Models},
 author = {Xiao, Guangxuan and Lin, Ji and Seznec, Mickael and Wu, Hao and Demouth, Julien and Han, Song},
 booktitle = {Proceedings of the 40th International Conference on Machine Learning},
 year = {2023}



We thank MIT-IBM Watson AI Lab, MIT AI Hardware Program, Amazon and MIT Science Hub, NVIDIA Academic Partnership Award, Qualcomm Innovation Fellowship, Microsoft Turing Academic Program, and NSF for supporting this research. We thank Haotian Tang, Aohan Zeng, Eric Lin, and Jilei Hou for the helpful discussions.

Team Members