Search Results for "nvidia"
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Showing 75 open source projects for "nvidia"
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NVIDIA Merlin
Library providing end-to-end GPU-accelerated recommender systems
...For more information, see NVIDIA Merlin on the NVIDIA developer website. Transform data (ETL) for preprocessing and engineering features. Accelerate your existing training pipelines in TensorFlow, PyTorch, or FastAI by leveraging optimized, custom-built data loaders. Scale large deep learning recommender models by distributing large embedding tables that exceed available GPU and CPU memory. -
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NVIDIA NeMo
Toolkit for conversational AI
NVIDIA NeMo, part of the NVIDIA AI platform, is a toolkit for building new state-of-the-art conversational AI models. NeMo has separate collections for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models. Each collection consists of prebuilt modules that include everything needed to train on your data. -
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NVIDIA AgentIQ
The NVIDIA AgentIQ toolkit is an open-source library
NVIDIA AgentIQ is an open-source toolkit designed to efficiently connect, evaluate, and accelerate teams of AI agents. It provides a framework-agnostic platform that integrates seamlessly with various data sources and tools, enabling developers to build composable and reusable agentic workflows. By treating agents, tools, and workflows as simple function calls, AgentIQ facilitates rapid development and optimization of AI-driven applications, enhancing collaboration and efficiency in complex tasks. -
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NVIDIA FLARE
NVIDIA Federated Learning Application Runtime Environment
NVIDIA Federated Learning Application Runtime Environment NVIDIA FLARE is a domain-agnostic, open-source, extensible SDK that allows researchers and data scientists to adapt existing ML/DL workflows(PyTorch, TensorFlow, Scikit-learn, XGBoost etc.) to a federated paradigm. It enables platform developers to build a secure, privacy-preserving offering for a distributed multi-party collaboration. -
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NVIDIA Isaac Sim
NVIDIA Isaac Sim is an open-source application on NVIDIA Omniverse
NVIDIA Isaac Sim is a high-fidelity robotics simulation platform built on NVIDIA Omniverse to develop, test, and validate AI-driven robots in physically accurate virtual environments. It supports a wide array of robotics formats (URDF, MJCF, CAD), includes GPU-accelerated physics, and features immersive RTX rendering and multisensory simulation. -
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NVIDIA Isaac Lab
Unified framework for robot learning built on NVIDIA Isaac Sim
Isaac Lab is an open-source modular robotics learning framework built atop Isaac Sim. It simplifies research workflows across reinforcement learning, imitation learning, and motion planning by offering robust, GPU-accelerated simulation with realistic sensor and physics fidelity—ideal for sim-to-real robot training. Compatible and optimized for use with Isaac Sim versions (e.g., Sim 5.0 and 4.5). GPU-accelerated, high-fidelity physics and sensor simulation suitable for complex learning... -
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NVIDIA NeMo Framework
Scalable generative AI framework built for researchers and developers
NVIDIA NeMo is a scalable, cloud-native generative AI framework aimed at researchers and PyTorch developers working on large language models, multimodal models, and speech AI (ASR and TTS), with growing support for computer vision. It provides collections of domain-specific modules and reference implementations that make it easier to pre-train, fine-tune, and deploy very large models on multi-GPU and multi-node infrastructure. -
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NVIDIA Isaac GR00T
NVIDIA Isaac GR00T N1.5 is the world's first open foundation model
NVIDIA Isaac‑GR00T N1.5 is an open-source foundation model engineered for generalized humanoid robot reasoning and manipulation skills. It accepts multimodal inputs—such as language and images—and uses a diffusion transformer architecture built upon vision-language encoders, enabling adaptive robot behaviors across diverse environments. -
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Sunshine
Self-hosted game stream host for Moonlight
Sunshine is an open-source self‑hosted cloud gaming server that implements NVIDIA’s GameStream protocol. Compatible with Moonlight clients across platforms, it supports low‑latency streaming via software or hardware encoding (AMD/Intel/NVIDIA) and offers a browser‑based control UI for pairing. -
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Transformer Engine
A library for accelerating Transformer models on NVIDIA GPUs
Transformer Engine (TE) is a library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper GPUs, to provide better performance with lower memory utilization in both training and inference. TE provides a collection of highly optimized building blocks for popular Transformer architectures and an automatic mixed precision-like API that can be used seamlessly with your framework-specific code. -
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Nvitop
An interactive NVIDIA-GPU process viewer and beyond
nvitop is an interactive NVIDIA device and process monitoring tool. It has a colorful and informative interface that continuously updates the status of the devices and processes. As a resource monitor, it includes many features and options, such as tree-view, environment variable viewing, process filtering, process metrics monitoring, etc. Beyond that, the package also ships a CUDA device selection tool nvisel for deep learning researchers. -
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Megatron
Ongoing research training transformer models at scale
...Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. -
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TensorRT Node for ComfyUI
Enables the best performance on NVIDIA RTX Graphics Cards
...This is particularly attractive for power users who run many generations or who host ComfyUI on dedicated hardware and want to squeeze out every bit of GPU performance. In short, it’s about taking ComfyUI from “it runs” to “it runs fast” on NVIDIA GPUs. -
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BioNeMo
BioNeMo Framework: For building and adapting AI models
BioNeMo is an AI-powered framework developed by NVIDIA for protein and molecular generation using deep learning models. It provides researchers and developers with tools to design, analyze, and optimize biological molecules, aiding in drug discovery and synthetic biology applications. -
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DeepSeek-V3
Powerful AI language model (MoE) optimized for efficiency/performance
...Evaluations indicate that it outperforms other open-source models and rivals leading closed-source models, achieving this with a training duration of 55 days on 2,048 Nvidia H800 GPUs, costing approximately $5.58 million. -
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CodeGeeX
CodeGeeX: An Open Multilingual Code Generation Model (KDD 2023)
...CodeGeeX also powers IDE plugins for VS Code and JetBrains, offering features like code completion, translation, debugging, and annotation. The model supports Ascend 910 and NVIDIA GPUs, with optimizations like quantization and FasterTransformer acceleration for faster inference. -
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clone-voice
A sound cloning tool with a web interface, using your voice
...The app is designed to be very easy to use: you download a precompiled package, double-click app.exe, and it launches a browser-based web interface where you control cloning and synthesis. It does not require an NVIDIA GPU to run basic tasks, although GPU acceleration can be used when available, making it accessible on modest machines. The tool supports around sixteen languages, including Chinese, English, Japanese, Korean, French, German, Italian, and others, and can capture reference voices directly from a microphone or from uploaded audio. -
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Ricks-Lab GPU Utilities
A set of utilities for monitoring and customizing GPU performance
...In order to get the maximum capability of these utilities, you should be running with a kernel that provides support for the GPUs you have installed. If using AMD GPUs, installing the latest AMD GPU driver or ROCm package may provide additional capabilities. If you have Nvidia GPUs installed, you should have Nvidia-smi installed in order for the utility reading of the cards to be possible. Writing to GPUs is currently only possible for compatible AMD GPUs on systems with appropriate kernel versions with the AMD ppfeaturemask set to enable this capability. -
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exo
Run your own AI cluster at home with everyday devices
Run your own AI cluster at home with everyday devices. Maintained by exo labs. Forget expensive NVIDIA GPUs, unify your existing devices into one powerful GPU, iPhone, iPad, Android, Mac, Linux, or pretty much any device. Now the default models, run 8B, 70B, and 405B parameter models on your own devices. -
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FastKoko
Dockerized FastAPI wrapper for Kokoro-82M text-to-speech model
FastKoko is a self-hosted text-to-speech server built around the Kokoro-82M model and exposed through a FastAPI backend. It is designed to be easy to deploy via Docker, with separate CPU and GPU images so that users can choose between pure CPU inference and NVIDIA GPU acceleration. The project exposes an OpenAI-compatible speech endpoint, which means existing code that talks to the OpenAI audio API can often be pointed at a Kokoro-FastAPI instance with minimal changes. It supports multiple languages and voicepacks and allows phoneme based generation for more accurate pronunciation and prosody. ... -
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CuPy
A NumPy-compatible array library accelerated by CUDA
CuPy is an open source implementation of NumPy-compatible multi-dimensional array accelerated with NVIDIA CUDA. It consists of cupy.ndarray, a core multi-dimensional array class and many functions on it. CuPy offers GPU accelerated computing with Python, using CUDA-related libraries to fully utilize the GPU architecture. According to benchmarks, it can even speed up some operations by more than 100X. CuPy is highly compatible with NumPy, serving as a drop-in replacement in most cases. ... -
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OpenHardwareMonitor
Free open source tool for real-time PC hardware sensor monitoring
...It provides real-time insights into key system metrics such as temperatures, fan speeds, voltages, load percentages, and clock speeds by reading directly from sensors embedded in CPUs, GPUs, motherboards, and storage devices. The tool supports a wide range of sensor hardware found on modern systems, including Intel and AMD processors, NVIDIA and AMD graphics cards, SMART temperature sensors on storage drives, and many motherboard monitoring chips. Users can view monitored data directly within the application, as customizable desktop gadgets, or through a system tray interface, making it easy to keep an eye on system health at a glance. OpenHardwareMonitor runs without the need for a traditional installation (it can be run portably) and on systems configured with the appropriate runtime (e.g., .NET or Mono). -
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BackgroundMattingV2
Real-Time High-Resolution Background Matting
Official repository for the paper Real-Time High-Resolution Background Matting. Our model requires capturing an additional background image and produces state-of-the-art matting results at 4K 30fps and HD 60fps on an Nvidia RTX 2080 TI GPU. -
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CogVideo
text and image to video generation: CogVideoX (2024) and CogVideo
CogVideo is an open source text-/image-/video-to-video generation project that hosts the CogVideoX family of diffusion-transformer models and end-to-end tooling. The repo includes SAT and Diffusers implementations, turnkey demos, and fine-tuning pipelines (including LoRA) designed to run across a wide range of NVIDIA GPUs, from desktop cards (e.g., RTX 3060) to data-center hardware (A100/H100). Current releases cover CogVideoX-2B, CogVideoX-5B, and the upgraded CogVideoX1.5-5B variants, plus image-to-video (I2V) models, with options for BF16/FP16/FP32—and INT8 quantized inference via TorchAO for memory-constrained setups. The codebase emphasizes practical deployment: prompt-optimization utilities (LLM-assisted long-prompt expansion), Colab notebooks, a Gradio web app, and multiple performance knobs (tiling/slicing, CPU offload, torch.compile, multi-GPU, and FA3 backends via partner projects). -
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InvokeAI
InvokeAI is a leading creative engine for Stable Diffusion models
...InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products. This fork is supported across Linux, Windows and Macintosh. Linux users can use either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm driver). We do not recommend the GTX 1650 or 1660 series video cards. They are unable to run in half-precision mode and do not have sufficient VRAM to render 512x512 images.
