{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"Provides NVIDIA TensorRT libraries for GPU-accelerated deep learning inference, packaged for CUDA 13 environments.","skillfed_tags":["gpu-inference","model-optimization","cuda-toolkit"],"use_cases":["Optimize and deploy large language models (LLMs) for low-latency inference on NVIDIA GPUs in production.","Convert ONNX or PyTorch models to TensorRT engines with quantization for edge deployment and reduced memory footprint.","Build real-time computer vision inference pipelines using optimized TensorRT engines from vision model checkpoints.","Benchmark and profile deep learning models to measure inference performance and identify optimization opportunities.","Integrate GPU-accelerated inference into microservices or Triton Inference Server deployments for scalable model serving."],"what_it_does":"tensorrt-cu13-libs is NVIDIA's inference optimization and execution engine for deep learning models on CUDA-capable GPUs. It provides compiled libraries and Python bindings to convert trained neural networks (from ONNX, PyTorch, TensorFlow, or direct API definition) into optimized TensorRT engines that execute with lower latency and higher throughput than native frameworks.\n\nVersion 11.X represents a major API redesign that removes legacy features: weakly-typed networks are replaced by strongly-typed networks, implicit quantization by explicit quantization, IPluginV2 by IPluginV3, and Python support now requires Python 3.10 or newer. The package is intended for developers building production inference services, embedded AI applications, and real-time inference pipelines where GPU acceleration and model optimization are critical.","worth_installing":"Yes, with conditions. Install if you need GPU-accelerated inference on CUDA 13 systems and can meet the system prerequisites (CUDA toolkit, Python 3.10+, compatible GPU). High install friction and proprietary licensing are trade-offs for significant inference performance gains. Not suitable if you lack CUDA infrastructure, require Python 3.9 support, or need open-source licensing."},"id":"tensorrt-cu13-libs","links":{"html":"https://skillfed.io/packages/tensorrt-cu13-libs","md":"https://skillfed.io/packages/tensorrt-cu13-libs.md","pypi":"https://pypi.org/project/tensorrt-cu13-libs/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-30","license_spdx":null,"license_treatment":"unclear","name":"tensorrt-cu13-libs","python_support":"unspecified","summary":"TensorRT Libraries"},"popularity":{"monthly_downloads":134615,"position":11469,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"11.2.1.2"}