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Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT | NVIDIA Technical Blog
Markus Nagel on Twitter: "Checkout our latest work with @mfournarakis on quantized training which brings us one step closer to achieve efficient on-device training." / Twitter
![SOLVED: Compute the relative effect of quantization error in the conversion for a 0.180V analog signal with an 8-bit ADC. The ADC has a full scale range (FSR) of 0V to 5V. SOLVED: Compute the relative effect of quantization error in the conversion for a 0.180V analog signal with an 8-bit ADC. The ADC has a full scale range (FSR) of 0V to 5V.](https://cdn.numerade.com/ask_images/da7a525561e14fc086d75f13c8fdc63f.jpg)
SOLVED: Compute the relative effect of quantization error in the conversion for a 0.180V analog signal with an 8-bit ADC. The ADC has a full scale range (FSR) of 0V to 5V.
![R] SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models - Massachusetts Institute of Technology and NVIDIA Guangxuan Xiao et al - Enables INT8 for LLM bigger than 100B parameters including R] SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models - Massachusetts Institute of Technology and NVIDIA Guangxuan Xiao et al - Enables INT8 for LLM bigger than 100B parameters including](https://preview.redd.it/r-smoothquant-accurate-and-efficient-post-training-v0-etmfxsu0id1a1.jpg?width=608&format=pjpg&auto=webp&s=130675efbf095112acc39c9c336ea3881936bc2b)