A unified library of SOTA model optimization techniques like quantization, distillation, pruning, neural architecture search, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.
A unified library of SOTA model optimization techniques like quantization, distillation, pruning, neural architecture search, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.
NVIDIA Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization, pruning, Neural Architecture Search (NAS), distillation, speculative decoding and sparsity to accelerate models.
[Input] Model Optimizer currently supports inputs of a Hugging Face, PyTorch or ONNX model.
Description and README excerpt from NVIDIA/Model-Optimizer on GitHub ↗.
NVIDIA/Model-Optimizer was first seen on GitHub · Trending Python today on Nov 5, 2026. Since then it has appeared in 8 checks on 2 different days. Its best position was #7 of 18, reached on Nov 5, 11:49 UTC. It dropped off the list after Nov 6, 08:49 UTC.
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Not at the moment. It was last on the list on Nov 6, 08:49 UTC, after 2 days of trending.
#7 on GitHub · Trending Python today, on Nov 5, 11:49 UTC. Trendoday checks this list every 180 minutes.
It was first seen on Nov 5, 2026 and has been on the list on 2 days (8 checks) since.
Mostly Python; it is licensed under Apache-2.0. It has 5531 stars and 800 forks.