Why open source rocks – a new SM750 (Silicon Motion GPU) HDMI Driver(github.com)

118 points by SillyUsername 1 month ago | 40 comments

Nvidia-Ingest: Multi-modal data extraction(github.com)

125 points by mihaid150 1 year ago | 35 comments

F* file system – file search that reads SSD directly bypassing OS kernel(github.com)

90 points by neogoose 4 months ago | 44 comments

Lumina – a statically typed web-native language for JavaScript and WASM(github.com)

52 points by light_ideas 6 months ago | 20 comments

MatX: Efficient C++17 GPU numerical computing library with Python-like syntax(github.com)

84 points by cl3misch 3 years ago | 64 comments

Xortran - A PDP-11 Neural Network With Backpropagation in Fortran IV(github.com)

34 points by rahen 11 months ago | 6 comments

Nvidia Warp: A Python framework for high performance GPU simulation and graphics(github.com)

453 points by jarmitage 2 years ago | 125 comments

GitHub repository for NVIDIA Warp, a Python framework for writing high-performance GPU simulation and graphics code.

Ollama now supports AMD graphics cards(ollama.com)

578 points by tosh 2 years ago | 194 comments

Ollama blog post from March 14, 2024 announcing preview support for running models on AMD graphics cards.

The first conformant M1 GPU driver(rosenzweig.io)

1545 points by todsacerdoti 3 years ago | 636 comments

Blog post announcing that Asahi Linux's reverse-engineered open source OpenGL ES 3.1 driver for Apple M1/M2 GPUs passed official conformance.

OpenAI and Nvidia announce partnership to deploy 10GW of Nvidia systems(openai.com)

441 points by meetpateltech 1 year ago | 569 comments

OpenAI announcement of a strategic partnership with NVIDIA to deploy 10 gigawatts of NVIDIA systems for its AI infrastructure.

OpenGL 3.1 on Asahi Linux(asahilinux.org)

483 points by simjue 3 years ago | 287 comments

Asahi Linux blog post announcing its Apple Silicon GPU drivers now support OpenGL 3.1 and OpenGL ES 3.0, up from OpenGL 2.1 and ES 2.0.

GPUs Go Brrr(hazyresearch.stanford.edu)

1056 points by nmstoker 2 years ago | 243 comments

Stanford Hazy Research blog post on what GPU hardware wants for efficient AI kernels, introducing the ThunderKittens CUDA kernel library.