NVIDIA TensorRT 10.0 Upgrades Usability, Performance, and AI Model Support
Table of Contents
- Feature Upgrades
- Weight Streaming
- NVIDIA TensorRT Model Optimizer 0.11
- Expanded Support for AI Models
- Summary
Feature Upgrades
- TensorRT 10.0 introduces INT4 Weight-Only Quantization (WoQ) with block quantization and improved memory allocation options.
- WoQ allows for refitting engines with weights without rebuilding the engine at runtime, resulting in a smaller plan file that can be refitted using weights from the ONNX model.
- Weight streaming option enables streaming network weights from host memory to device memory during execution.
- TensorRT Model Optimizer Python APIs are available for model optimizations to accelerate inference.
Weight Streaming
- TensorRT can stream network weights during execution instead of loading them into device memory at engine load time, improving memory usage and performance.
NVIDIA TensorRT Model Optimizer 0.11
- A comprehensive library of post-training and training-in-the-loop model optimizations is included.
- Model Optimizer enables quantization, sparsity, and distillation to reduce model complexity and optimize inference speed.
- MLPerf Inference v4.0 showcases a 1.3x speedup with post-training sparsity on top of FP8 quantization using TensorRT-LLM on NVIDIA H100 with Llama 270B.
Expanded Support for AI Models
- TensorRT-LLM, an open-source library for optimizing LLM inference, now supports weight-stripped engines added in TensorRT 10.0.
- Enhanced support for various AI models and optimizations adds versatility to the TensorRT ecosystem.
Summary
- NVIDIA TensorRT 10.0 brings significant upgrades, including INT4 quantization, weight streaming, Model Optimizer, and expanded AI model support.
- These features enhance performance, usability, and model optimization capabilities within the TensorRT framework.
