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 Enterprise
NVIDIA 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.
1

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