---
id: tensorrt-cu12-bindings
version: "11.2.1.2"
license: Proprietary
license_treatment: unclear
maintenance: active
---

# tensorrt-cu12-bindings — A high performance deep learning inference library

License: unclear · Maintenance: active · Downloads: 297.2K/mo

## What it is and what it does

tensorrt-cu12-bindings is a Python interface to NVIDIA's TensorRT inference engine, version 11.2.1.2, compiled for CUDA 12. It allows developers to load, optimize, and execute deep learning models on NVIDIA GPUs with minimal latency and maximum throughput. The package provides strongly-typed network APIs and explicit quantization support as part of TensorRT 11.X's redesigned architecture.

The bindings are distributed as platform-specific wheels for modern Python versions on Linux x86_64 and Windows x86_64. Installation is straightforward via pip, but requires CUDA 12 and compatible GPU drivers to be present on the system. There are no Python runtime dependencies; the package is a thin wrapper around compiled TensorRT libraries.

Use it for:

- Optimize and deploy pre-trained deep learning models for low-latency inference on NVIDIA GPUs.
- Build quantized inference engines using explicit quantization APIs to reduce model size and improve throughput.
- Integrate custom CUDA kernels and operations via IPluginV3 plugin API for domain-specific inference acceleration.
- Benchmark and profile inference performance across different batch sizes and precision levels.
- Deploy inference services in production environments where model latency and throughput are critical.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides Python bindings for NVIDIA TensorRT 11.2.1.2 compiled for CUDA 12, enabling deep learning inference acceleration on x86_64 Linux and Windows systems.

Yes, if you have CUDA 12 and an NVIDIA GPU and need to deploy deep learning inference at scale. The package is actively maintained, has no known vulnerabilities, and is part of a mature inference platform. Verify licensing terms with NVIDIA for your use case, and confirm that CUDA 12 and compatible drivers are already installed before attempting installation.

## Install

pip install tensorrt-cu12-bindings
uv add tensorrt-cu12-bindings
poetry add tensorrt-cu12-bindings

## Installing tensorrt-cu12-bindings

Before you install: Medium install friction: platform-specific wheels for Python 3.8–3.14 on Linux (manylinux_2_28) and Windows x86_64 only. No runtime dependencies, but requires CUDA 12 and compatible GPU drivers already present. Active maintenance with recent releases.

License in practice: Licensed as Proprietary with unclear treatment. Users should verify licensing terms with NVIDIA before deploying in production or commercial contexts.

Quickstart:

pip install tensorrt-cu12-bindings

import tensorrt as trt

logger = trt.Logger(trt.Logger.WARNING)
builder = trt.Builder(logger)

Requires CUDA 12 runtime and compatible NVIDIA GPU drivers; only available for Python 3.8–3.14 on Linux x86_64 (manylinux_2_28) or Windows x86_64.

Verify before relying:

- Whether this package alone is sufficient for inference or if additional TensorRT components must be installed separately.
- Exact CUDA 12 minor version compatibility and minimum driver version required.
- Whether the package includes prebuilt TensorRT libraries or requires a separate TensorRT GA build installation.
- Supported precision levels and quantization capabilities beyond explicit quantization mentioned in the description.

## Package facts

- License: Proprietary (unclear)
- Python support: unspecified
- Install friction: medium
- Maintenance: active
- Downloads: 297.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

tensorrt cuda 12 python bindings, nvidia inference acceleration, deep learning model optimization, tensorrt gpu inference, cuda 12 bindings, neural network inference library, tensorrt python api, gpu-inference, model-optimization, cuda-12

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