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NVIDIA Model Optimizer: PTQ Cuts Latency Without Retraining

NVIDIA Model Optimizer converts FP16/FP32 models to INT8/INT4 for faster inference without retraining, using post-training quantization techniques.

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Someone found NVIDIA’s Model Optimizer tooling useful for speeding up AI inference without touching training code.

Post-Training Quantization (PTQ) is the fastest path - converts existing FP16/FP32 models to INT8 or INT4 for immediate latency reduction:

pip install nvidia-modelopt
import modelopt.torch.quantization as mtq

# Quantize to INT8
model = mtq.quantize(model, config=mtq.INT8_DEFAULT_CFG)

Full docs: https://github.com/NVIDIA/TensorRT-Model-Optimizer

Three main approaches:

  • PTQ: Converts models post-training, fastest to implement
  • QAT: Recovers accuracy during fine-tuning with low-precision constraints
  • Pruning + Distillation: Permanently shrinks model size

Works with PyTorch models out of the box. Most useful when deploying to NVIDIA GPUs where INT8/INT4 kernels are hardware-accelerated.

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