A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 11, 21:15 UTC
[Misc] Rename TensorRT Model Optimizer to Model Optimizer (#30091)
Signed-off-by: Zhiyu Cheng <zhiyuc@nvidia.com>
Zhiyu committed
Dec 7, 2025 at 23:05 UTC
cd00c443d2272e5325dc6d730616f0a68ad533d1
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docs/features/quantization/README.md
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@@ -14,7 +14,7 @@ Contents:
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- [INT4 W4A16](int4.md)
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- [INT8 W8A8](int8.md)
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- [FP8 W8A8](fp8.md)
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-- [NVIDIA TensorRT Model Optimizer](modelopt.md)
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+- [NVIDIA Model Optimizer](modelopt.md)
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- [AMD Quark](quark.md)
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- [Quantized KV Cache](quantized_kvcache.md)
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- [TorchAO](torchao.md)
docs/features/quantization/modelopt.md
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-# NVIDIA TensorRT Model Optimizer
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+# NVIDIA Model Optimizer
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-The [NVIDIA TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer) is a library designed to optimize models for inference with NVIDIA GPUs. It includes tools for Post-Training Quantization (PTQ) and Quantization Aware Training (QAT) of Large Language Models (LLMs), Vision Language Models (VLMs), and diffusion models.
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+The [NVIDIA Model Optimizer](https://github.com/NVIDIA/Model-Optimizer) is a library designed to optimize models for inference with NVIDIA GPUs. It includes tools for Post-Training Quantization (PTQ) and Quantization Aware Training (QAT) of Large Language Models (LLMs), Vision Language Models (VLMs), and diffusion models.
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We recommend installing the library with:
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## Quantizing HuggingFace Models with PTQ
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-You can quantize HuggingFace models using the example scripts provided in the TensorRT Model Optimizer repository. The primary script for LLM PTQ is typically found within the `examples/llm_ptq` directory.
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+You can quantize HuggingFace models using the example scripts provided in the Model Optimizer repository. The primary script for LLM PTQ is typically found within the `examples/llm_ptq` directory.
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Below is an example showing how to quantize a model using modelopt's PTQ API:
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