NVIDIA/accelerated-computing-hub

NVIDIA curated collection of educational resources related to general purpose GPU programming.

Jupyter Notebook

2,044

911 commits

updated Oct 12, 2026

See the code

README

NVIDIA Accelerated Computing Hub

This repository is a home for open learning materials related to GPU computing. You will find user guides, tutorials, and other works freely available for all learners interested in GPU computing.

The intention is to create a living project where documents, examples, best practices, optimizations, and new features can become both visible and accessible to users quickly and easily.

Tutorials and Syllabi

The following interactive tutorials are available and can be used on NVIDIA Brev or Google Colab. See the documentation for more information on creating and deploying Brev Launchables using this content.

ContentDocker ComposeBrev InstanceBrev Provider
CUDA Tile TutorialGenerated ComposeA10GAWS or any provider with Flexible Ports
CUDA C++ Tutorialdocker-compose.ymlL40S, L4, or T4Crusoe or any other with Flexible Ports
Standard Parallelism Tutorialdocker-compose.yml4xL4, 2xL4, 2xL40S, or 1x L40SGCP, AWS, or any other with Flexible Ports and Linux 6.1.24+, 6.2.11+, or 6.3+ (for HMM)
Accelerated Python Tutorialdocker-compose.ymlL40S, L4, or T4; A10G or newer Ampere/Ada/Blackwell for CUDA Tile; 4xL4 or 2xL4 for distributedCrusoe or any other with Flexible Ports; host driver must support CUDA 13
NVIDIA Warp Tutorialdocker-compose.ymlL40S, L4, or T4Crusoe or any other with Flexible Ports
Sim2Real Blogsdocker-compose.ymlL40S, L4, or T4Crusoe or any other with Flexible Ports
nvmath-python Tutorialdocker-compose.yml4xL4, 2xL4, 2xL40S, or 1x L40SCrusoe or any other with Flexible Ports
CUDA Python Tutorial - CuPy, cuDF, CCCL, & Kernels - 8 Hoursdocker-compose.ymlL40S, L4, or T4Crusoe or any other with Flexible Ports
CUDA Python Tutorial - CuPy, SIMT, & Tile - 8 Hoursdocker-compose.ymlA10GAWS or any provider with Flexible Ports
CUDA Python Tutorial - cuda.core & CCCL - 2 Hoursdocker-compose.ymlL40S, L4, or T4Crusoe or any other with Flexible Ports
PyHPC Tutorial - NumPy, CuPy, & mpi4py - 4 Hoursdocker-compose.yml4xL4, 2xL4, 2xL40S, or 1x L40SCrusoe or any other with Flexible Ports; host driver must support CUDA 13
PyHPC Tutorial - CuPy, Kernels, MPI, JAX, OMP, Interop - 2 Daysdocker-compose.ymlL40S, L4, or T4Crusoe or any other with Flexible Ports; host driver must support CUDA 13
GPU Deployment Tutorial
Newton Robot Tasks Tutorialdocker-compose.ymlNVIDIA GPU with a CUDA 13.1-compatible driver; local CPU path availableAny provider with Flexible Ports; see tutorial requirements

License

All written materials (user guides, documentation, presentations) are subject to Creative Commons CC BY-NC-SA 4.0.

All codes (notebook code, coding examples) are subject to Apache License, Version 2.0.

Contact

For additional help, please use NVIDIA's CUDA Developer Forums.

NVIDIA/accelerated-computing-hub

NVIDIA curated collection of educational resources related to general purpose GPU programming.

Jupyter Notebook

2,044

911 commits

updated Oct 12, 2026

See the code

README

NVIDIA Accelerated Computing Hub

This repository is a home for open learning materials related to GPU computing. You will find user guides, tutorials, and other works freely available for all learners interested in GPU computing.

The intention is to create a living project where documents, examples, best practices, optimizations, and new features can become both visible and accessible to users quickly and easily.

Tutorials and Syllabi

The following interactive tutorials are available and can be used on NVIDIA Brev or Google Colab. See the documentation for more information on creating and deploying Brev Launchables using this content.

ContentDocker ComposeBrev InstanceBrev Provider
CUDA Tile TutorialGenerated ComposeA10GAWS or any provider with Flexible Ports
CUDA C++ Tutorialdocker-compose.ymlL40S, L4, or T4Crusoe or any other with Flexible Ports
Standard Parallelism Tutorialdocker-compose.yml4xL4, 2xL4, 2xL40S, or 1x L40SGCP, AWS, or any other with Flexible Ports and Linux 6.1.24+, 6.2.11+, or 6.3+ (for HMM)
Accelerated Python Tutorialdocker-compose.ymlL40S, L4, or T4; A10G or newer Ampere/Ada/Blackwell for CUDA Tile; 4xL4 or 2xL4 for distributedCrusoe or any other with Flexible Ports; host driver must support CUDA 13
NVIDIA Warp Tutorialdocker-compose.ymlL40S, L4, or T4Crusoe or any other with Flexible Ports
Sim2Real Blogsdocker-compose.ymlL40S, L4, or T4Crusoe or any other with Flexible Ports
nvmath-python Tutorialdocker-compose.yml4xL4, 2xL4, 2xL40S, or 1x L40SCrusoe or any other with Flexible Ports
CUDA Python Tutorial - CuPy, cuDF, CCCL, & Kernels - 8 Hoursdocker-compose.ymlL40S, L4, or T4Crusoe or any other with Flexible Ports
CUDA Python Tutorial - CuPy, SIMT, & Tile - 8 Hoursdocker-compose.ymlA10GAWS or any provider with Flexible Ports
CUDA Python Tutorial - cuda.core & CCCL - 2 Hoursdocker-compose.ymlL40S, L4, or T4Crusoe or any other with Flexible Ports
PyHPC Tutorial - NumPy, CuPy, & mpi4py - 4 Hoursdocker-compose.yml4xL4, 2xL4, 2xL40S, or 1x L40SCrusoe or any other with Flexible Ports; host driver must support CUDA 13
PyHPC Tutorial - CuPy, Kernels, MPI, JAX, OMP, Interop - 2 Daysdocker-compose.ymlL40S, L4, or T4Crusoe or any other with Flexible Ports; host driver must support CUDA 13
GPU Deployment Tutorial
Newton Robot Tasks Tutorialdocker-compose.ymlNVIDIA GPU with a CUDA 13.1-compatible driver; local CPU path availableAny provider with Flexible Ports; see tutorial requirements

License

All written materials (user guides, documentation, presentations) are subject to Creative Commons CC BY-NC-SA 4.0.

All codes (notebook code, coding examples) are subject to Apache License, Version 2.0.

Contact

For additional help, please use NVIDIA's CUDA Developer Forums.

Significant stargazers

Carol Willing

1,565 followers · starred Jan 2025

Henry Schreiner

945 followers · starred Jul 2025

Gabriel Majeri

260 followers · starred May 2026

Mateusz Sokół

71 followers · starred Mar 2025