Inside NVIDIA container ecosystems

Overview

TensorRT is NVIDIA's high-performance inference optimizer and runtime for deploying deep learning models on NVIDIA GPUs. Use it when you need lower latency, higher throughput, FP16/INT8 optimization, or production GPU serving from ONNX or TensorFlow/PyTorch exports.

When to Use

Use this skill when:

• a model already works in PyTorch/TensorFlow but inference is too slow,
• you need FP16 or INT8 deployment on NVIDIA GPUs,
• you are deploying vision, NLP, or embedding models in production,
• you want to serve optimized engines via Triton Inference Server,
• or you need to benchmark GPU inference carefully instead of guessing.

Install / Environment

TensorRT is usually installed via NVIDIA packages, Docker images, or NGC containers rather than plain pip.

Typical paths:

# Inside NVIDIA container ecosystems
# Use an NGC PyTorch or TensorRT container

# ONNX graph simplification often helps before conversion
uv pip install onnx onnxruntime onnxsim polygraphy

Fastest Common Workflow: ONNX -> TensorRT Engine

1. Export model to ONNX.
2. Validate ONNX with ONNX Runtime.
3. Build TensorRT engine with trtexec.
4. Benchmark latency/throughput.
5. Integrate engine into app or Triton.

Export from PyTorch to ONNX

import torch