# TensorRT

NVIDIA released the cuda.bindings.cudla module in cuda-bindings 13.3.0, a thin Cython-generated Python layer over the cuDLA C++ runtime that lets Jetson AGX Orin applications create DLA devices, load networks, register buffers and submit inference without writing C++. The module targets hybrid mode, where DLA tasks are enqueued on a CUDA stream alongside GPU preprocessing and postprocessing, and the same API applies on RTX Spark. The release notes that standalone mode, which needs NvSci buffers and sync primitives, is not supported in this version.

NVIDIA released the cuda.bindings.cudla module in cuda-bindings 13.3.0, a thin Cython-generated Python layer over the cuDLA C++ runtime that lets Jetson AGX Orin applications create DLA devices, load networks, register buffers and submit inference without writing C++. The module targets hybrid mode, where DLA tasks are enqueued on a CUDA stream alongside GPU preprocessing and postprocessing, and the same API applies on RTX Spark. The release notes that standalone mode, which needs NvSci buffers and sync primitives, is not supported in this version.

NVIDIA released the Global Performance Tuner in TensorRT 11.1, turning compilation scheduling from a single default strategy into customizable build routes. Optimization had relied on heuristics distilled from internal KPI models, and accumulating rules could regress on unseen external models. The tuner validates each candidate solution against a specified accuracy criterion and measures end-to-end inference performance, then selects the fastest solution among those that meet the requirement.

NVIDIA released the Global Performance Tuner in TensorRT 11.1, turning compilation scheduling from a single default strategy into customizable build routes. Optimization had relied on heuristics distilled from internal KPI models, and accumulating rules could regress on unseen external models. The tuner validates each candidate solution against a specified accuracy criterion and measures end-to-end inference performance, then selects the fastest solution among those that meet the requirement.

NVIDIA's Global Performance Tuner, released in TensorRT 11.1, represents compiler scheduling choices as searchable build routes, a concrete combination of compiler knob values, and selects the fastest measured end-to-end engine among those that meet a user-supplied accuracy boundary. On ten evaluated workloads, with each default build route normalized to 1.0x, the best eligible route found during tuning delivered 1.14x to 1.63x. Accuracy decides which candidates are acceptable and performance ranks only those, so a tuned route is tied to its model, input shapes, target GPU and software versions.

NVIDIA's Global Performance Tuner, released in TensorRT 11.1, represents compiler scheduling choices as searchable build routes, a concrete combination of compiler knob values, and selects the fastest measured end-to-end engine among those that meet a user-supplied accuracy boundary. On ten evaluated workloads, with each default build route normalized to 1.0x, the best eligible route found during tuning delivered 1.14x to 1.63x. Accuracy decides which candidates are acceptable and performance ranks only those, so a tuned route is tied to its model, input shapes, target GPU and software versions.