SearchSearch thousands of GPU-optimized Containers, pretrained Models, SDKs, and Helm charts—ready to accelerate AI, digital twins, and HPC from cloud to edge.
NVIDIA AI Enterprise
NVIDIA AI Enterprise
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  • NVIDIA NIM
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    NIM Container GPUs
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  • Displaying 15 results
    NVIDIA Developer Program
    NVIDIA NIM for GPU accelerated Llama 2 70B inference through OpenAI compatible APIs
    Container
    NVIDIA
    NVIDIA
    Isaac Sim
    NVIDIA Isaac Sim™ is a robotics and AI simulation application framework built on NVIDIA Omniverse™. Isaac Sim has essential features for building virtual robotic worlds and experiments.
    Container
    NVIDIA
    NVIDIA
    PyG
    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
    NVIDIA Developer Program
    A widely used model for predicting the 3D structures of proteins from their amino acid sequences. This version of the container supports multimers, i.e. proteins made up of 2 or more polypeptide chains.
    Container
    NVIDIA Developer Program
    NVIDIA
    NVIDIA
    MAISI NIM
    MAISI NIM generated high-quality synthetic CT images with or without anatomical annotations.
    Container
    Turn workload evaluation and optimization requests into a fully automated, end-to-end workflow across many specialized micro-services with the Flywheel orchestrator control-plane service.
    Container
    Synthetic data generation
    Container
    Deploy the NVIDIA Data Flywheel Foundational Blueprint on Kubernetes using Helm charts for scalable, production-ready environments.
    Helm Chart
    Nemotron-4-340B-Base
    Model
    A docker-compose file to simplify quickstart deployments of NeMo Data Designer.
    Resource
    Jupyter Notebook for training FasterRCNN with Rendered.ai user generated datasets.
    Resource
    This collection contains FastPitch and Spectrogram Enhancer models. Main use case is English ASR domain fine-tuning. Direct TTS use is not advised.
    Model
    This resource consists of the Consistency Distilled Dataset used for Proteina-Atomistica model training.
    Resource
    Collection
    Cosmos World Foundation Models: A family of highly performant pre-trained world foundation models purpose-built for generating physics-aware videos and world states for physical AI development.
    815
    Collection
    This collections consists of the training data for the Proteina-Atomistica all-atom protein structure generative model.
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