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  • SearchSearch thousands of GPU-optimized Containers, pretrained Models, SDKs, and Helm charts—ready to accelerate AI, digital twins, and HPC from cloud to edge.
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  • Displaying 273 results
    TAO Toolkit
    NVIDIA
    Docker containers distributed as part of the TAO Toolkit package
    Container
    Triton Inference Server
    NVIDIA
    Triton Inference Server is an open source software that lets teams deploy trained AI models from any framework, from local or cloud storage and on any GPU- or CPU-based infrastructure in the cloud, data center, or embedded devices.
    Container
    PyTorch
    NVIDIA
    PyTorch is a GPU accelerated tensor computational framework. Functionality can be extended with common Python libraries such as NumPy and SciPy. Automatic differentiation is done with a tape-based system at the functional and neural network layer levels.
    Container
    TensorFlow
    Google
    TensorFlow is an open source platform for machine learning. It provides comprehensive tools and libraries in a flexible architecture allowing easy deployment across a variety of platforms and devices.
    Container
    TensorRT LLM Release
    NVIDIA
    TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs.
    Container
    TensorRT
    NVIDIA
    NVIDIA TensorRT is a C++ library that facilitates high-performance inference on NVIDIA graphics processing units (GPUs). TensorRT takes a trained network and produces a highly optimized runtime engine that performs inference for that network.
    Container
    vLLM
    NVIDIA
    vLLM is a fast and easy-to-use library for LLM inference and serving. The NVIDIA vLLM NGC Container is optimized for GPU acceleration, and contains a validated set of libraries that enable and optimize GPU performance.
    Container
    CUDA Deep Learning
    NVIDIA
    CUDA is a parallel computing platform and programming model that enhances computing performance using NVIDIA GPUs. CUDA Deep Learning integrates networking and GPU-accelerated libraries like cuDNN, cuTensor, NCCL, HPC-x, and the CUDA Toolkit.
    Container
    NVIDIA Optimized Deep Learning Framework powered by Apache MXNet
    Apache Software Foundation
    NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet is a deep learning framework that allows you to mix the flavors of symbolic programming and imperative programming to maximize efficiency and productivity.
    Container
    Allegro Trains Agent
    Allegro AI
    Allegro Trains delivers an optimized, seamless, and scalable solution for training on DGX machines with ML-Ops and experiment management features.
    Container
    Dynamo vLLM Runtime
    NVIDIA
    The Dynamo vLLM runtime image is a containerized build of Dynamo + vLLM which serves as the base runtime environment for vLLM based inference with Dynamo's distributed inference framework.
    Container
    NVIDIA Magnum IO Developer Environment
    NVIDIA
    NVIDIA Magnum IO is the I/O technologies from NVIDIA and Mellanox that enable applications at scale. The Magnum IO Developer Environment container allows developers to begin scaling their applications on a laptop, desktop, workstation, or in the cloud.
    Container
    Dynamo Tensorrt-LLM Runtime
    NVIDIA
    The Dynamo TensorRT-LLM runtime image is a containerized build of Dynamo + TensorRT-LLM which serves as the base runtime environment for tensorrt-llm based inference with Dynamo's distributed inference framework.
    Container
    JAX
    NVIDIA
    JAX is a framework for high-performance numerical computing and machine learning research. It includes Numpy-like APIs, automatic differentiation, XLA acceleration and simple primitives for scaling across GPUs and supports an ecosystem of libraries.
    Container
    CUDA GL
    NVIDIA
    CUDA is a parallel computing platform and programming model that enables dramatic increases in computing performance by harnessing the power of the NVIDIA GPUs. These images extend the CUDA images to include OpenGL support through libglvnd.
    Container
    Dynamo SGLang Runtime
    NVIDIA
    The Dynamo SGLang runtime image is a containerized build of Dynamo + SGLang which serves as the base runtime environment for sglang based inference with Dynamo's distributed inference framework.
    Container
    Dynamo Platform
    NVIDIA
    A comprehensive Helm chart for deploying the NVIDIA Dynamo operator and its dependencies
    Helm Chart
    TensorRT LLM Develop
    NVIDIA
    TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs.
    Container
    PyG
    NVIDIA
    PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
    Container
    Dynamo kubernetes-operator
    NVIDIA
    kubernetes-operator is a container that runs as part of the Dynamo cloud platform. Dynamo cloud is a kubernetes platform for deploying and managing inference services. This container manages the lifecycle of Dynamo inference deployments in kubernetes.
    Container
    Kaldi
    NVIDIA
    Kaldi is an open-source software framework for speech processing.
    Container
    Getting Start with Recommender System
    NVIDIA
    The Variational Autoencoder for collaborative filtering focuses on providing recommendations.
    Resource
    Triton Inference Server PB October 2025 (PB 25h2)
    NVIDIA
    Triton Inference Server PB October 2025 (PB 25h2) offers a 9-month lifecycle for API stability, with monthly patches for high and critical software vulnerabilities.
    Container
    Merlin PyTorch
    NVIDIA
    The Merlin PyTorch container allows users to do preprocessing and feature engineering with NVTabular, and then train a deep-learning based recommender system model with PyTorch, and serve the trained model on Triton Inference Server.
    Container
    ...

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