{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"Applies state-of-the-art model optimization techniques\u2014quantization, pruning, Neural Architecture Search, distillation, speculative decoding, and sparsity\u2014to accelerate PyTorch, Hugging Face, and ONNX models for deployment.","skillfed_tags":["model-compression","quantization","inference-optimization"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"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."},"id":"nvidia-modelopt","links":{"html":"https://skillfed.io/packages/nvidia-modelopt","md":"https://skillfed.io/packages/nvidia-modelopt.md","pypi":"https://pypi.org/project/nvidia-modelopt/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-06","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"nvidia-modelopt","python_support":"supports_current","summary":"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."},"popularity":{"monthly_downloads":496448,"position":6336,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.45.0"}