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

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…

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…

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Source:: NVIDIA