nvidia-modelopt
Nvidia Model Optimizer: A unified library of SOTA model optimization techniques like quantization, pruning, Neural Architecture Search (NAS), distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.
Decision gist · record as of 2026-08-14
Yes, if you deploy PyTorch or Hugging Face models on NVIDIA hardware and need to reduce inference latency or memory footprint. The library is actively maintained, permissively licensed, and integrates cleanly with the NVIDIA inference ecosystem. Install friction is low and security record is clean. Not applicable for CPU-only or non-NVIDIA deployments.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PyTorch and NVIDIA GPU support; quantization and optimization workflows typically need CUDA-capable hardware for practical speedup.
- Low friction install; pure Python wheel.
- Active maintenance with recent commits and 3442 repository stars.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions.
last release 2026-07-06 (39 days) · last repo commit 2026-08-14 · 3,442 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 496,448 downloads/mo, #6,336 on PyPI
Alternatives
Verify before relying
pip install nvidia-modelopt
from nvidia_modelopt import quantization
import torch
model = torch.nn.Linear(10, 5)
quantized = quantization.quantize(model)- Whether the package requires NVIDIA-specific GPU hardware or works on CPU for development/testing.
- Performance gains and memory reduction percentages claimed in the description (e.g., 2.6× throughput) for typical model sizes.
- Compatibility with specific downstream frameworks (TensorRT-LLM, vLLM, SGLang) beyond the documented integration points.
What it is and what it does
NVIDIA Model Optimizer is a library for compressing and accelerating deep learning models through a suite of optimization techniques. It accepts PyTorch, Hugging Face, or ONNX models as input, applies techniques like quantization (FP8, NVFP4), pruning, distillation, and Neural Architecture Search to reduce model size and latency, then exports optimized checkpoints ready for deployment in inference frameworks like TensorRT-LLM, vLLM, and SGLang.
The package is designed for teams optimizing large language models and vision models for production inference. It integrates with NVIDIA Megatron-LM and Hugging Face Accelerate for training-time optimization, and provides Python APIs to compose multiple techniques together. Dependencies include torch, numpy, omegaconf, pydantic, and other ML-stack standards.
Use it for
- Quantize large language models to lower precision (FP8, NVFP4) for faster inference without retraining.
- Prune and distill LLMs to reduce model size while maintaining accuracy for deployment on resource-constrained hardware.
- Apply post-training quantization to Hugging Face transformer models for immediate export to TensorRT or vLLM.
- Combine multiple optimization techniques (pruning + distillation + quantization) in a single workflow for maximum throughput gains.
- Export optimized diffusion models for faster image generation on NVIDIA GPUs.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you deploy PyTorch or Hugging Face models on NVIDIA hardware and need to reduce inference latency or memory footprint.
The library is actively maintained, permissively licensed, and integrates cleanly with the NVIDIA inference ecosystem. Install friction is low and security record is clean. Not applicable for CPU-only or non-NVIDIA deployments.
Install
nvidia-modelopt on PyPI
Before you install
Low friction install; pure Python wheel. Active maintenance with recent commits and 3442 repository stars. Depends on 15 runtime packages including torch, numpy, and omegaconf—standard for ML workflows.
Requires PyTorch and NVIDIA GPU support; quantization and optimization workflows typically need CUDA-capable hardware for practical speedup.
License in practice
Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions.
Quickstart
pip install nvidia-modelopt
from nvidia_modelopt import quantization
import torch
model = torch.nn.Linear(10, 5)
quantized = quantization.quantize(model)Verify before relying
- Whether the package requires NVIDIA-specific GPU hardware or works on CPU for development/testing.
- Performance gains and memory reduction percentages claimed in the description (e.g., 2.6× throughput) for typical model sizes.
- Compatibility with specific downstream frameworks (TensorRT-LLM, vLLM, SGLang) beyond the documented integration points.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 15 packagesninjanumpynvidia-ml-pypackagingsetuptoolstorchtqdmPyYAMLomegaconfpulppydanticregexrichsafetensorsscipy |
| Maintenance | Actively maintained 39 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 496,448 / month, #6,336 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: nvidia_modelopt-0.45.0-py3-none-any.whl
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