Smoothquant

Smoothquant Information Guide

1. Introduction on Smoothquant
2. Core Information
3. Developments
4. Detailed Analysis
5. Conclusion

Introduction on Smoothquant

SmoothQuant - Accurate and Efficient Post-Training Quantization for Large Language Models In this video, we look into SmoothQ Algorithm and Paper: Paper: Pseudocode Open Source ... Large language models (LLMs) show excellent performance but are compute- and memory-intensive. Quantization can reduce ... 00:00 Introduction to LLM Quantization 02:15 What is Quantization? 04:45 Post-Training Quantization (PTQ) vs. QAT 07:30 GPTQ ... Quantization is an excellent technique to compress Large Language Models (LLM) and accelerate their inference. In this video ... Seminar date : 2024.07.05 # Seminar contents Paper Review Seminar # Paper Title Xiao, Guangxuan, et al. "

Pseudo-lab (‪-lab‬ ) EfficientLLM study Presenter: 김승우 Date: 2025/09/30 Paper:

Core Information

Developments

SmoothQuant : Accurate and Efficient Post Training Quantization for Large Langu

LLM Quantization Explained: GPTQ, AWQ, QLoRA, GGUF and More

05.09.2023 SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Final Presentation CS104 SmoothQuant (15 Min)

SmoothQuant : run LLM on CPU

Deep Dive: Quantizing Large Language Models, part 1

[IDSL Paper Review] SmoothQuant

CS104 SmoothQuant Final Presentation

[Paper Review] SmoothQuant

Detailed Analysis

Data is compiled from public records and verified media reports.

Last Updated: June 19, 2026

Conclusion

Disclaimer: Disclaimer: Details estimates are based on publicly available data, media reports, and financial analysis. Actual numbers may vary.