tensorrt-cu12
A high performance deep learning inference library
Decision gist · record as of 2026-08-14
Yes, if you have CUDA GPU infrastructure and need production-grade inference acceleration. High install friction and proprietary licensing require upfront commitment to NVIDIA's ecosystem. Active maintenance and no known vulnerabilities are positive signals. Not suitable for development without dedicated GPU hardware or for environments where proprietary licensing is prohibited.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires TensorRT GA build v11.2.1.2 pre-installed and Python >=3.10 (3.9 and older no longer supported in 11.X).
- High install friction: requires CUDA and system-level TensorRT libraries (tensorrt_cu12_libs, tensorrt_cu12_bindings).
- Package is actively maintained with recent releases, but installation demands pre-existing GPU infrastructure and native dependencies.
License · maintenance · safety
Proprietary (unclear) — Licensed as Proprietary with unclear treatment. Terms are not publicly specified; review NVIDIA's licensing documentation before deploying in commercial or restricted environments.
last release 2026-07-30 (15 days) · last repo commit 2026-08-04 · 13,250 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 226,275 downloads/mo, #9,202 on PyPI
Alternatives
Verify before relying
pip install tensorrt-cu12
import tensorrt as trt
logger = trt.Logger(trt.Logger.WARNING)
builder = trt.Builder(logger)- Whether tensorrt-cu12 can be installed standalone or requires manual TensorRT GA build download and extraction.
- Exact CUDA versions (12.9 or 13.3) supported by this package variant.
- Exact compatibility matrix between Python versions 3.10–3.14 and this package version.
- Whether cuDNN 8.9 is required or optional for this package.
What it is and what it does
tensorrt-cu12 is the Python interface to NVIDIA's TensorRT 11.2.1.2 inference engine, optimized for CUDA 12 GPUs. It compiles and executes deep learning models with minimal latency and memory overhead, supporting import paths from ONNX, PyTorch, and HuggingFace. Version 11 introduced breaking changes: weakly-typed networks and implicit quantization have been removed in favor of strongly-typed networks and explicit quantization; IPluginV2 has been replaced by IPluginV3; and Python 3.9 and older are no longer supported.
The package depends on tensorrt_cu12_libs and tensorrt_cu12_bindings, which provide compiled libraries and low-level bindings. Installation requires pre-existing CUDA and TensorRT GA build infrastructure. It is intended for developers deploying inference workloads on NVIDIA GPUs where model throughput and latency are critical, not for training or development on CPU-only systems.
Use it for
- Compile trained models (ONNX, PyTorch) into optimized TensorRT engines for low-latency serving on NVIDIA GPUs.
- Deploy LLMs, vision models, and encoder-NLP architectures with explicit quantization and graph optimization for production inference.
- Benchmark and profile inference performance across different batch sizes and precision modes.
- Integrate custom layers via IPluginV3 to extend TensorRT's operator coverage for specialized model architectures.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have CUDA GPU infrastructure and need production-grade inference acceleration.
High install friction and proprietary licensing require upfront commitment to NVIDIA's ecosystem. Active maintenance and no known vulnerabilities are positive signals. Not suitable for development without dedicated GPU hardware or for environments where proprietary licensing is prohibited.
Install
tensorrt-cu12 on PyPI
Before you install
High install friction: requires CUDA and system-level TensorRT libraries (tensorrt_cu12_libs, tensorrt_cu12_bindings). Package is actively maintained with recent releases, but installation demands pre-existing GPU infrastructure and native dependencies.
Requires TensorRT GA build v11.2.1.2 pre-installed and Python >=3.10 (3.9 and older no longer supported in 11.X).
License in practice
Licensed as Proprietary with unclear treatment. Terms are not publicly specified; review NVIDIA's licensing documentation before deploying in commercial or restricted environments.
Quickstart
pip install tensorrt-cu12
import tensorrt as trt
logger = trt.Logger(trt.Logger.WARNING)
builder = trt.Builder(logger)Verify before relying
- Whether tensorrt-cu12 can be installed standalone or requires manual TensorRT GA build download and extraction.
- Exact CUDA versions (12.9 or 13.3) supported by this package variant.
- Exact compatibility matrix between Python versions 3.10–3.14 and this package version.
- Whether cuDNN 8.9 is required or optional for this package.
Package facts
| License | Proprietary unclear |
| Python support | Supports the current Python release >=3.8 |
| Install friction | High. Source build required |
| Runtime dependencies | 2 packagestensorrt_cu12_libstensorrt_cu12_bindings |
| Maintenance | Actively maintained 15 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 226,275 / month, #9,202 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_cu12-11.2.1.2.tar.gz
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See also tensorrt-cu12-bindings · tensorrt-cu12-libs · tensorrt-cu13-bindings · tensorrt-cu13-libs · tensorrt-cu13 · tensorrt · dyNET38 · sit4onnx · transformer-engine-cu12 · torch