{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"}],"enrichment":{"capability":"TensorRT compiles and optimizes deep learning models for deployment on NVIDIA GPUs, reducing latency and memory footprint through layer fusion, quantization, and kernel auto-tuning.","skillfed_tags":["gpu-inference","model-optimization","nvidia-cuda"],"use_cases":["Compile ONNX or PyTorch models into optimized TensorRT engines for low-latency inference on NVIDIA GPUs.","Deploy large language models or vision transformers with reduced memory footprint using quantization and kernel optimization.","Benchmark and profile model inference performance across different batch sizes and precision levels on target hardware.","Integrate optimized inference engines into production services for real-time inference workloads.","Migrate from TensorRT 10.X to 11.X by refactoring weakly-typed networks to Strongly Typed Networks and updating custom plugins to IPluginV3."],"what_it_does":"TensorRT is NVIDIA's inference optimization library that compiles trained deep learning models into optimized GPU-resident engines. It reduces model size, latency, and memory consumption through layer fusion, quantization, and kernel auto-tuning, making it the standard tool for deploying neural networks on NVIDIA GPUs in production.\n\nVersion 11.2.1.2 is a major release that removes legacy APIs (weakly-typed networks, implicit quantization, IPluginV2) in favor of modern alternatives (Strongly Typed Networks, Explicit Quantization, IPluginV3). Installation requires the TensorRT GA build plus CUDA toolkit and system dependencies; the package is actively maintained and widely used (197616 monthly downloads), but carries high install friction due to compiled dependencies and GPU-specific requirements.","worth_installing":"Yes, if you deploy deep learning models on NVIDIA GPUs and need production-grade inference optimization. TensorRT is the standard tool for this task, actively maintained, and widely adopted (top 15000 packages). However, installation requires CUDA toolkit, system-level build tools, and the TensorRT GA build v11.2.1.2; proprietary licensing requires license review for commercial use; and version 11.X breaks backward compatibility with 10.X APIs. Install only if you have GPU infrastructure and can meet compilation prerequisites."},"id":"tensorrt","links":{"html":"https://skillfed.io/packages/tensorrt","md":"https://skillfed.io/packages/tensorrt.md","pypi":"https://pypi.org/project/tensorrt/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-30","license_spdx":null,"license_treatment":"unclear","name":"tensorrt","python_support":"supports_current","summary":"TensorRT Metapackage"},"popularity":{"monthly_downloads":197616,"position":9754,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"11.2.1.2"}