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Post-Training Quantization of LLMs with NVIDIA NeMo and NVIDIA TensorRT Model Optimizer

GIXnews
Illustration showing models and NeMo.

As large language models (LLMs) are becoming even bigger, it is increasingly important to provide easy-to-use and efficient deployment paths because the cost of…Illustration showing models and NeMo.

As large language models (LLMs) are becoming even bigger, it is increasingly important to provide easy-to-use and efficient deployment paths because the cost of serving such LLMs is becoming higher. One way to reduce this cost is to apply post-training quantization (PTQ), which consists of techniques to reduce computational and memory requirements for serving trained models. In this post…

Source

Source:: NVIDIA