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588 repositories
- C++ and Python support for the CUDA Quantum programming model for heterogeneous quantum-classical workflows
- TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and support state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in performant way.
- Kubernetes enhancements for Network Topology Aware Gang Scheduling & Autoscaling
- CUDA Core Compute Libraries
- A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
- BioNeMo Framework: For building and adapting AI models in drug discovery at scale
- A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper, Ada and Blackwell GPUs, to provide better performance with lower memory utilization in both training and inference.
- A Python framework for accelerated simulation, data generation and spatial computing.
- Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods
- Documentation repository for NVIDIA Cloud Native Technologies
- NVIDIA Federated Learning Application Runtime Environment
- AIStore: scalable storage for AI applications
- Open-source deep-learning framework for exploring, building and deploying AI weather/climate workflows.
- NeMo Retriever extraction is a scalable, performance-oriented document content and metadata extraction microservice. NeMo Retriever extraction uses specialized NVIDIA NIM microservices to find, contextualize, and extract text, tables, charts and images that you can use in downstream generative applications.