nvidia-model-optimizer

name	nvidia-model-optimizer
description	NVIDIA Model Optimizer — quantization, pruning, distillation, and speculative decoding for accelerating LLMs, diffusion models, and vision models on NVIDIA GPUs.
tags	["nvidia-model-optimizer","quantization","pruning","distillation","modelopt","tensorrt","zorai"]

Overview

NVIDIA Model Optimizer provides state-of-the-art model optimization techniques — quantization (FP8, INT4, NVFP4), pruning, knowledge distillation, and speculative decoding — for models deployable on TensorRT-LLM, SGLang, and vLLM.

Installation

uv pip install nvidia-modelopt

Basic Quantization

import modelopt.torch.quantization as mtq

# FP8 post-training quantization
quant_cfg = mtq.FP8_DEFAULT_CFG
mtq.quantize(model, quant_cfg, forward_loop=calib_loop)

# Export for TensorRT-LLM
from modelopt.torch.export import export_tensorrt_llm_checkpoint
export_tensorrt_llm_checkpoint(model, "model.pt", dtype="fp8")

Pruning + Distillation

import modelopt.torch.pruning as mtp
import modelopt.torch.distill as mtd

# Prune
pruned = mtp.prune(model, ratio=0.3, structure="2:4_sparse")

# Distill
teacher = load_teacher_model()
student = mtd.distill(student_model, teacher, kal="logit", alpha=0.5)

References

• NVIDIA ModelOpt docs
• NVIDIA ModelOpt GitHub