NVIDIA's latest release of TensorRT, version 10.0, brings forth a multitude of improvements aimed at enhancing usability, performance, and support for AI models. Notable upgrades include simplified installation processes facilitated by updated Debian and RPM metapackages, streamlining the installation of TensorRT libraries for both C++ and Python users. Among the new features introduced is the Debug Tensors API, allowing developers to mark tensors for debugging during build time, thereby aiding in the identification of issues within the graph at runtime.

Performance enhancements are a highlight of TensorRT 10.0, with the introduction of INT4 Weight-Only Quantization (WoQ) featuring block quantization and improved memory allocation options. The inclusion of weight compression using INT4 facilitates efficient memory usage, particularly in scenarios where memory bandwidth constraints affect General Matrix Multiply (GEMM) operation performance. Block Quantization, on the other hand, offers finer granularity settings in quantization scales, contributing to enhanced precision during runtime. Additionally, runtime allocation strategies have been refined to allow for the specification of allocation strategies for execution context device memory.

TensorRT 10.0 also introduces features such as weight-stripped engines and weight streaming, designed to simplify the deployment of larger models on smaller GPUs. Weight-stripped engines enable significant compression of engine size, while weight streaming enables models with weights larger than available GPU memory to run by streaming weights from host memory to device memory during network execution. These features collectively enhance performance and efficiency in deploying AI models.

Moreover, TensorRT Model Optimizer 0.11, included in TensorRT 10.0, offers a comprehensive suite of post-training and training-in-the-loop model optimizations. Advanced techniques like Post-Training Quantization (PTQ), Quantization Aware Training (QAT), and Sparsity are employed to reduce model complexity and optimize inference speed. The Model Optimizer facilitates accelerated inference by simulating quantized checkpoints for PyTorch and ONNX models, leveraging existing runtime and compiler optimizations in TensorRT. Additionally, support for Nsight Deep Learning Designer enhances the visual diagnosis of network inference performance, aiding in the tuning of models for optimal performance.