CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs.
A high-throughput and memory-efficient inference and serving engine for LLMs
SGLang is a fast serving framework for large language models and vision language models.
Build and run Docker containers leveraging NVIDIA GPUs
Instant neural graphics primitives: lightning fast NeRF and more
kaldi-asr/kaldi is the official location of the Kaldi project.
Burn is a next generation Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.
Open3D: A Modern Library for 3D Data Processing
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.
OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.
Containers for machine learning
CUDA Templates and Python DSLs for High-Performance Linear Algebra
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Samples for CUDA Developers which demonstrates features in CUDA Toolkit
📚LeetCUDA: Modern CUDA Learn Notes with PyTorch for Beginners🐑, 200+ CUDA Kernels, Tensor Cores, HGEMM, FA-2 MMA.🎉
Created by Nvidia
Released June 23, 2007