801
Dépôts publics
274 375
Total des étoiles
29 984
Abonnés
NVIDIA Corporation possède une présence significative sur GitHub, avec une large gamme de dépôts publics. Les langages principaux utilisés incluent Python, C++, et Jupyter Notebook, avec des projets notables tels que NemoClaw et Megatron-LM, qui se concentrent sur l'optimisation et la sécurité des modèles d'IA.
Run agents like Hermes, LangChain Deep Agents, and OpenClaw more securely inside NVIDIA OpenShell with managed inference
Ongoing research training transformer models at scale
NVIDIA Linux open GPU kernel module source
Security scanner for AI agent skills. Detect vulnerabilities, malicious patterns, security risks, prompt injection, data exfiltration, and supply-chain risks in Claude Code, Codex, and MCP skills before you install them.
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
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. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way.
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
NVIDIA Cosmos is an open platform of world models, datasets, and tools that enables developers to build Physical AI for robots, autonomous vehicles, smart infrastructure, and more.
PersonaPlex code.
CUDA Templates and Python DSLs for High-Performance Linear Algebra
cuDF - GPU DataFrame Library
Samples for CUDA Developers which demonstrates features in CUDA Toolkit
the LLM vulnerability scanner
OpenShell is the safe, private runtime for autonomous AI agents.
NVIDIA Isaac GR00T N1.7 - A Foundation Model for Generalist Robots.
A Python framework for GPU-accelerated simulation, robotics, and machine learning.
NVIDIA cuML: GPU-Accelerated Machine Learning
Build and run containers leveraging NVIDIA GPUs
NVIDIA device plugin for Kubernetes
A unified library of SOTA model optimization techniques like quantization, distillation, pruning, neural architecture search, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.
A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit and 4-bit floating point (FP8 and FP4) precision on Hopper, Ada and Blackwell GPUs, to provide better performance with lower memory utilization in both training and inference.
CUDA Python: Performance meets Productivity
Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end.
Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods
NeMo Retriever Library is a scalable, performance-oriented document content and metadata extraction microservice. NeMo Retriever Library uses specialized NVIDIA NIM microservices to find, contextualize, and extract text, tables, charts and images that you can use in downstream generative applications.
NVIDIA GPU Operator creates, configures, and manages GPUs in Kubernetes
The NVIDIA NeMo Agent toolkit is an open-source library for efficiently connecting and optimizing teams of AI agents.
CUDA Core Compute Libraries
TensorRT Extension for Stable Diffusion Web UI
AIStore: scalable storage for AI applications
NCCL Tests
This repo contains the source code for RULER: What’s the Real Context Size of Your Long-Context Language Models?
NVIDIA DLSS is a new and improved deep learning neural network that boosts frame rates and generates beautiful, sharp images for your games
Tools for building GPU clusters
Documentation of NVIDIA chip/hardware interfaces
Router that virtually distributes inference across connected devices in the home.
C++ and Python support for the CUDA Quantum programming model for heterogeneous quantum-classical workflows
Open-source deep-learning framework for exploring, building and deploying AI weather/climate workflows.
GPU accelerated decision optimization
NVIDIA cuDF for Apache Spark plugin - accelerate Apache Spark with GPUs
NVIDIA Federated Learning Application Runtime Environment
cuDNN Frontend is NVIDIA's modern, open-source entry point to the cuDNN library and a growing collection of high-performance open-source kernels.
cuVS - a library for vector search and clustering on the GPU
DreamGen: Nvidia GEAR Lab's initiative to solve the robotics data problem using world models
SOMA BVH to humanoid robot motion retargeting library built with Newton and NVIDIA Warp
High-performance, light-weight C++ LLM and VLM Inference Software for Physical AI
Our inference and training framework to run on the Cosmos Models
NeMo text processing for ASR and TTS
JAX-Toolbox
Multi-tier framework for evaluating AI agent skills with quality gates, semantic overlap detection, synthetic evaluation dataset generation, and live agent evaluation that measures how skills affect agent behavior.
Tooling for optimized, validated, and reproducible GPU-accelerated AI runtime in Kubernetes
This repo contains CUDA-Q Academic materials, including self-paced Jupyter notebook modules for building and optimizing hybrid quantum-classical algorithms using CUDA-Q.
NVSentinel detects and remediates GPU faults on Kubernetes nodes
The unified framework for sim & real robot teleoperation
NVIDIA driver control panel
NVIDIA Resiliency Extension is a python package for framework developers and users to implement fault-tolerant features. It improves the effective training time by minimizing the downtime due to failures and interruptions.
Ancillary open source software to support confidential computing on NVIDIA GPUs
Examples for Recommenders - easy to train and deploy on accelerated infrastructure.
NVIDIA k8s device plugin for Kubevirt
The CUDA target for Numba
Help shape the future of Project G-Assist
From PyTorch model to end-to-end TensorRT inference experience in two commands—AI-native, cross-platform, and built for the best possible user experience.
The developer-first platform for scaling complex Physical AI workloads across heterogeneous compute—unifying training GPUs, simulation clusters, and edge devices in a simple YAML
Platform for deploying and routing GPU-accelerated inference, streaming, and batch workloads at scale.
Data representations, APIs, and tools for high quality AV and robotics applications
The NVIDIA GPU driver container allows the provisioning of the NVIDIA driver through the use of containers.
An Operator for deployment and maintenance of NVIDIA NIMs and NeMo microservices in a Kubernetes environment.
ALCHEMI Toolkit is a developer toolkit for accelerating training and inference for AI in chemistry and material science.
NVIDIA driver installer
TensorIR is a lightweight NVIDIA-owned MLIR compiler frontend for expressing tensor computations and lowering them to NVIDIA CUDA Tile IR.
Ubuntu kernels which are optimized for NVIDIA server systems
Exemplar Performance provides recipes in ready-to-use templates for evaluating performance of specific AI use cases across hardware and software combinations.
Spark RAPIDS MLlib – accelerate Apache Spark MLlib with GPUs
NVIDIA Inference Benchmarks provide recipes in ready-to-use templates for evaluating platform speed. Validate your platform across specific AI use cases across hardware and software combinations.
XR AI
NVIDIA Design System and UI Agent Harness for AI/ML Factories, Robotics, and Autonomous Vehicles
Load the NVIDIA kernel module and create NVIDIA character device files
User tools for Spark RAPIDS
NVIDIA cuDF plugin JNI For Apache Spark
A Kubernetes Operator to manage Node OS customizations.
A collection of useful Go libraries for use with NVIDIA GPU management tools
Communication patterns for AI, built on top of NCCL device and host APIs
cuDF plugin Benchmarks – benchmark sets and utilities for the NVIDIA cuDF plugin for Apache Spark
A Scalable (and Optionally, Data-Parallel) ANARI Multi-GPU Path Tracer
NVIDIA NeMo Fabric
DAQIRI connects high bandwidth streaming sensor data to the NVIDIA software ecosystem
Tool to unpack or parse PLDM (Platform Level Data Model v1.3.0 or lesser) firmware update files.
This repository provides a starter kit for sports intelligence built on NVIDIA AI stack: playbooks, training recipes, and inference for Multimodal Language Models.
🧪 OpenShell's Research Journal
OpenStack Storage (Swift). Mirror of code maintained at opendev.org.
Official NVIDIA/srt-slurm sweep configs for benchmarking LLMs across NVIDIA GPUs and frameworks, spanning aggregated and disaggregated serving on single- and multi-node setups.
Aucune description fournie pour ce dépôt.
Web-based user interface built on Vue.js for managing OpenBMC systems
Non-PLDM firmware update infrastructure
Remotely mount images for the host through the BMC
A machine learning compiler for GPUs, CPUs, and ML accelerators
Run-time JSON driven system configuration manager
yaml-sigil-spec Rust YamlSigil trait and DTO surface
YAML-preserving + Protobuf round-trip in-toto signing format, seeking evolutionary hardening through public collaboration.
yaml-sigil-spec Rust implementation workspace (core + api libs)
NVIDIA développe divers projets sur GitHub, notamment des outils pour l'IA, des bibliothèques pour le calcul haute performance, et des modèles de langage. Des dépôts comme SkillSpector et TensorRT-LLM témoignent de leur engagement envers la sécurité et l'efficacité.
NVIDIA utilise principalement Python, C++, Jupyter Notebook, et d'autres langages comme Go et Rust. Ces langages soutiennent leur large éventail de projets, allant des modèles d'IA aux bibliothèques de traitement de données.
Oui, tous les dépôts de NVIDIA sur GitHub sont publics. Cela permet à la communauté de consulter, d'utiliser et de contribuer aux projets, favorisant ainsi la transparence et la collaboration dans le développement des technologies d'IA.
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