nvidia-nvimgcodec-cu12
NVIDIA nvimgcodec for CUDA 12.
Decision gist · record as of 2026-08-14
Yes, if you have a compatible NVIDIA GPU with CUDA 12 and need GPU-accelerated image codec operations. The library is actively maintained, has no known vulnerabilities, and offers a clean plugin architecture. No, if you lack GPU hardware or require CPU-only image processing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires CUDA 12 runtime and compatible GPU; wheels available only for x86_64, aarch64, and Windows platforms.
- Medium install friction due to platform-specific wheels (x86_64, aarch64, Windows) and CUDA 12 dependency.
- Active maintenance with recent commits and no known vulnerabilities.
License · maintenance · safety
Apache License 2.0 (permissive) — Apache License 2.0 is permissive, allowing commercial and private use with minimal restrictions.
last release 2026-07-14 (31 days) · last repo commit 2026-07-14 · 156 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 184,570 downloads/mo, #10,030 on PyPI
Alternatives
Verify before relying
pip install nvidia-nvimgcodec-cu12
# For JPEG support:
pip install nvidia-nvimgcodec-cu12[nvjpeg]
import nvidia_nvimgcodec as nvimgcodec
# Use codec operations via the unified interface- Specific codec operations and API surface beyond the unified interface framework
- Performance benchmarks or throughput claims for accelerated operations
- Compatibility matrix between optional plugins and CUDA versions
What it is and what it does
nvImageCodec is NVIDIA's GPU-accelerated image codec library designed as an extensible framework. It provides a unified interface for codec operations, with optional plugins for nvjpeg, nvjpeg2k, and nvtiff formats. The package is built specifically for CUDA 12 and supports Python 3.9 through 3.14.
The library offloads image encoding and decoding to the GPU, reducing CPU overhead for image processing workloads. Installation includes optional dependencies for specific codec support—you can install just the base library or add nvjpeg, nvjpeg2k, nvtiff, or all plugins together. Platform support is limited to x86_64, aarch64, and Windows due to the nature of GPU-specific wheels.
Use it for
- Accelerate batch image decoding in computer vision pipelines using GPU hardware instead of CPU.
- Encode or decode JPEG images at scale in data preprocessing for machine learning workflows.
- Process TIFF images with GPU acceleration in scientific or medical imaging applications.
- Build custom codec plugins on top of the nvImageCodec framework for specialized image formats.
- Reduce latency in real-time image processing by offloading codec operations to the GPU.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have a compatible NVIDIA GPU with CUDA 12 and need GPU-accelerated image codec operations.
The library is actively maintained, has no known vulnerabilities, and offers a clean plugin architecture. No, if you lack GPU hardware or require CPU-only image processing.
Install
nvidia-nvimgcodec-cu12 on PyPI
Before you install
Medium install friction due to platform-specific wheels (x86_64, aarch64, Windows) and CUDA 12 dependency. Active maintenance with recent commits and no known vulnerabilities.
Requires CUDA 12 runtime and compatible GPU; wheels available only for x86_64, aarch64, and Windows platforms.
License in practice
Apache License 2.0 is permissive, allowing commercial and private use with minimal restrictions.
Quickstart
pip install nvidia-nvimgcodec-cu12
# For JPEG support:
pip install nvidia-nvimgcodec-cu12[nvjpeg]
import nvidia_nvimgcodec as nvimgcodec
# Use codec operations via the unified interfaceVerify before relying
- Specific codec operations and API surface beyond the unified interface framework
- Performance benchmarks or throughput claims for accelerated operations
- Compatibility matrix between optional plugins and CUDA versions
Package facts
| License | Apache License 2.0 permissive |
| Python support | Supports the current Python release <3.15,>=3.9 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 31 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 184,570 / month, #10,030 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9 |
Evidence: nvidia_nvimgcodec_cu12-0.9.0.20-py3-none-manylinux_2_28_aarch64.whl; nvidia_nvimgcodec_cu12-0.9.0.20-py3-none-manylinux_2_28_x86_64.whl; nvidia_nvimgcodec_cu12-0.9.0.20-py3-none-win_amd64.whl
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See also nvidia-libnvcomp-cu12 · pynvvideocodec · nvidia-cusparse-cu12 · torchcodec · nvidia-cudnn-cu12 · decord2 · nvidia-cuda-runtime-cu12 · nvidia-cusolver-cu11 · nvidia-cuda-runtime-cu11 · nvidia-cufile-cu12