{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"}],"enrichment":{"capability":"Provides NVIDIA TensorRT libraries for CUDA 12 environments, enabling optimized deep learning model inference on NVIDIA GPUs.","skillfed_tags":["gpu-inference","nvidia-cuda","model-optimization"],"use_cases":["Deploy pre-optimized deep learning models for inference on NVIDIA GPUs in production environments","Accelerate inference latency for computer vision, NLP, and audio models on CUDA 12 systems","Build inference servers or microservices that require low-latency model execution","Optimize model throughput for batch inference workloads on NVIDIA data center GPUs","Integrate GPU-accelerated inference into Python applications targeting CUDA 12 infrastructure"],"what_it_does":"tensorrt-cu12-libs is a binary distribution of NVIDIA TensorRT's core libraries compiled for CUDA 12 environments. It provides the runtime components needed to load, optimize, and execute deep learning models on NVIDIA GPUs with low latency and high throughput. The package is part of TensorRT 11.X, which introduced breaking changes from the 10.X series, including removal of weakly-typed networks, implicit quantization, and IPluginV2 APIs in favor of their strongly-typed and explicit counterparts.\n\nThis is a library package intended for deployment environments where model inference is the primary goal. It does not include model training capabilities or the full TensorRT build toolchain. Installation requires pre-existing CUDA 12 support on the system and is most commonly used in conjunction with model conversion tools (ONNX, Torch-TensorRT, or the Network Definition API) to prepare models for inference. The high install friction reflects the size and specificity of the binary payload.","worth_installing":"Yes, if you need GPU-accelerated inference on CUDA 12 systems and accept the proprietary license terms. The package is actively maintained, has no known vulnerabilities, and is widely used (top 15000 PyPI packages). Install friction is high due to binary size, but that is inherent to the use case. Verify CUDA 12 compatibility and licensing requirements before production deployment."},"id":"tensorrt-cu12-libs","links":{"html":"https://skillfed.io/packages/tensorrt-cu12-libs","md":"https://skillfed.io/packages/tensorrt-cu12-libs.md","pypi":"https://pypi.org/project/tensorrt-cu12-libs/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-30","license_spdx":null,"license_treatment":"unclear","name":"tensorrt-cu12-libs","python_support":"unspecified","summary":"TensorRT Libraries"},"popularity":{"monthly_downloads":194262,"position":9841,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"11.2.1.2"}