tensorrt-cu12-libs
TensorRT Libraries
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires CUDA 12.x runtime and compatible NVIDIA GPU drivers; Python >= 3.10 and <= 3.14.x; TensorRT GA build v11.2.1.2 may need to be downloaded separately depending on installation method.
- High install friction due to large binary dependencies (tensorrt_cu12_libs-11.2.1.2.tar.gz).
- Active maintenance with recent releases; repository shows strong community engagement (13250 stars) and current development activity (last commit 2026-08-04).
License · maintenance · safety
Proprietary (unclear) — Licensed as Proprietary with unclear treatment. Users should verify licensing terms with NVIDIA before deploying in production or commercial contexts, as proprietary restrictions may apply.
last release 2026-07-30 (15 days) · last repo commit 2026-08-04 · 13,250 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 194,262 downloads/mo, #9,841 on PyPI
Alternatives
Verify before relying
pip install tensorrt-cu12-libs
import tensorrt as trt
logger = trt.Logger(trt.Logger.WARNING)
builder = trt.Builder(logger)- Whether this package includes the full TensorRT runtime or only CUDA 12 library bindings
- Exact Python version support (description mentions 3.10–3.14.x but package metadata does not specify)
- Whether CUDA 12.9 or 13.3 is required or if other 12.x versions are supported
- Licensing restrictions for commercial or proprietary use beyond the 'unclear' classification
What it is and 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.
This 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.
Use it for
- 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
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
tensorrt-cu12-libs on PyPI
Before you install
High install friction due to large binary dependencies (tensorrt_cu12_libs-11.2.1.2.tar.gz). Active maintenance with recent releases; repository shows strong community engagement (13250 stars) and current development activity (last commit 2026-08-04).
Requires CUDA 12.x runtime and compatible NVIDIA GPU drivers; Python >= 3.10 and <= 3.14.x; TensorRT GA build v11.2.1.2 may need to be downloaded separately depending on installation method.
License in practice
Licensed as Proprietary with unclear treatment. Users should verify licensing terms with NVIDIA before deploying in production or commercial contexts, as proprietary restrictions may apply.
Quickstart
pip install tensorrt-cu12-libs
import tensorrt as trt
logger = trt.Logger(trt.Logger.WARNING)
builder = trt.Builder(logger)Verify before relying
- Whether this package includes the full TensorRT runtime or only CUDA 12 library bindings
- Exact Python version support (description mentions 3.10–3.14.x but package metadata does not specify)
- Whether CUDA 12.9 or 13.3 is required or if other 12.x versions are supported
- Licensing restrictions for commercial or proprietary use beyond the 'unclear' classification
Package facts
| License | Proprietary unclear |
| Python support | Not specified |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Actively maintained 15 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 194,262 / month, #9,841 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersLicense :: Other/Proprietary LicenseProgramming Language :: Python :: 3 |
Evidence: tensorrt_cu12_libs-11.2.1.2.tar.gz
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “tensorrt cuda 12 inference”
- tensorrt-cu12-libsProvides NVIDIA TensorRT libraries for CUDA 12 environments, enabling…
- tensorrt-cu12-bindingsProvides Python bindings for NVIDIA TensorRT 11.2.1.2 compiled for…
- tensorrt-cu12Provides Python bindings for NVIDIA TensorRT 11, enabling…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also tensorrt-cu12 · tensorrt-cu13-libs · tensorrt · tensorrt-cu12-bindings · tensorrt-cu13 · tensorrt-cu13-bindings · sit4onnx · nvidia-cublas-cu11 · transformer-engine-cu12 · transformer-engine