Cuda
Showing 36 of 44 projects
A C/C++ library for efficient, cross-platform LLM inference with extensive hardware support and quantization.
A high-throughput, memory-efficient inference and serving engine for large language models (LLMs).
A fast open framework for deep learning with a focus on expression, speed, and modularity.
Real-time multi-person keypoint detection library for body, face, hands, and foot estimation.
An open-source, high-performance platform for developing, testing, and deploying autonomous vehicles.
An open source neural network framework in C and CUDA, known for YOLO real-time object detection models.
A high-performance serving framework for large language models and multimodal models, delivering low-latency and high-throughput inference.
An offline desktop application for transcribing and translating audio/video files, live recordings, and YouTube links using OpenAI's Whisper.
A unified deep learning toolkit for describing neural networks as computational graphs, supporting feed-forward DNNs, CNNs, and RNNs/LSTMs.
A deprecated wrapper that enabled Docker containers to access NVIDIA GPU resources.
A comprehensive open-source toolkit for speech recognition research and development.
NVIDIA's SDK for high-performance deep learning inference optimization and deployment on NVIDIA GPUs.
A fast, expressive, and header-only C++ library for building task-parallel programs with static, dynamic, and conditional task graphs.
A NumPy/SciPy-compatible array library for GPU-accelerated computing with Python, supporting NVIDIA CUDA and AMD ROCm.
A GPU-accelerated DataFrame library for tabular data processing, part of the RAPIDS data science suite.
Go language bindings for OpenCV 4, enabling computer vision applications with support for CUDA, DNN, and OpenVINO.
A flexible Python deep learning framework using define-by-run dynamic computational graphs for neural network research.
An open-source machine learning framework for building classical, deep, or hybrid ML applications with a focus on performance and portability.
A suite of GPU-accelerated machine learning algorithms with scikit-learn compatible APIs for 10-50x faster performance on large datasets.
A PyTorch library providing GPU-accelerated tools for 3D deep learning, including differentiable rendering and geometric operations.
A C++ parallel algorithms library that enables high-performance computing on GPUs and multicore CPUs with a productivity-focused interface.
A general-purpose tensor library for parallel computing across CPUs, GPUs, and hardware accelerators.
A library of optimized communication primitives for multi-GPU and multi-node collective operations.
A modular container build system providing the latest AI/ML packages for NVIDIA Jetson and JetPack-L4T.
A fast parallel implementation of the Connectionist Temporal Classification (CTC) loss function for CPU and GPU.
A granular, multi-language shader library for real-time graphics, supporting GLSL, HLSL, Metal, WGSL, and CUDA.
A realtime CPU/GPU profiler hosted in a single C file with a remote web viewer for performance analysis.
PyTorch implementation of FlowNet 2.0 for optical flow estimation using deep neural networks.
A deep learning framework for research, development, and production with flexible Python API and C++ core.
A C++ programming model for writing performance-portable applications targeting all major HPC platforms.
A ROS package for real-time object detection in camera images using YOLO (V3) on GPU and CPU.
A ROS package for real-time object detection in camera images using YOLO (V3) on GPU and CPU.
A lightweight gradient-based local planner for quadrotors that eliminates ESDF construction, achieving planning times around 1ms.
NVIDIA's implementation of the C++ Standard Library for CUDA C++ development.
A command-line tool for creating reproducible, container-based development environments for AI/ML workflows.
A collection of GPU-accelerated graph analytics libraries for creating, manipulating, and executing scalable graph algorithms.
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