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.

Content	Docker Compose	Brev Instance	Brev Provider
CUDA Tile Tutorial	Generated Compose	A10G	AWS or any provider with Flexible Ports
CUDA C++ Tutorial	docker-compose.yml	L40S, L4, or T4	Crusoe or any other with Flexible Ports
Standard Parallelism Tutorial	docker-compose.yml	4xL4, 2xL4, 2xL40S, or 1x L40S	GCP, AWS, or any other with Flexible Ports and Linux 6.1.24+, 6.2.11+, or 6.3+ (for HMM)
Accelerated Python Tutorial	docker-compose.yml	L40S, L4, or T4; A10G or newer Ampere/Ada/Blackwell for CUDA Tile; 4xL4 or 2xL4 for distributed	Crusoe or any other with Flexible Ports; host driver must support CUDA 13
NVIDIA Warp Tutorial	docker-compose.yml	L40S, L4, or T4	Crusoe or any other with Flexible Ports
Sim2Real Blogs	docker-compose.yml	L40S, L4, or T4	Crusoe or any other with Flexible Ports
nvmath-python Tutorial	docker-compose.yml	4xL4, 2xL4, 2xL40S, or 1x L40S	Crusoe or any other with Flexible Ports
CUDA Python Tutorial - CuPy, cuDF, CCCL, & Kernels - 8 Hours	docker-compose.yml	L40S, L4, or T4	Crusoe or any other with Flexible Ports
CUDA Python Tutorial - CuPy, SIMT, & Tile - 8 Hours	docker-compose.yml	A10G	AWS or any provider with Flexible Ports
CUDA Python Tutorial - cuda.core & CCCL - 2 Hours	docker-compose.yml	L40S, L4, or T4	Crusoe or any other with Flexible Ports
PyHPC Tutorial - NumPy, CuPy, & mpi4py - 4 Hours	docker-compose.yml	4xL4, 2xL4, 2xL40S, or 1x L40S	Crusoe or any other with Flexible Ports; host driver must support CUDA 13
PyHPC Tutorial - CuPy, Kernels, MPI, JAX, OMP, Interop - 2 Days	docker-compose.yml	L40S, L4, or T4	Crusoe or any other with Flexible Ports; host driver must support CUDA 13
GPU Deployment Tutorial
Newton Robot Tasks Tutorial	docker-compose.yml	NVIDIA GPU with a CUDA 13.1-compatible driver; local CPU path available	Any 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.