Optimizing Matrix Multiplication on RDNA3(seb-v.github.io)

112 points by skidrow 1 year ago | 22 comments

MorphoHDL: A minimalistic language for growing circuits(paradigms-of-intelligence.github.io)

87 points by jacktang 3 months ago | 8 comments

GPU Puzzles(github.com)

345 points by cgadski 2 years ago | 35 comments

GitHub repository by Sasha Rush offering interactive puzzles in a notebook for learning CUDA GPU programming.

Symbolic Learning in Python and Julia(github.com)

59 points by alex_hirner 5 days ago | 11 comments

GitHub repository for PySR, an open-source library for symbolic regression and equation discovery in Python with a Julia backend.

Show HN: CUA-S1 – A System One Model for Computer Use(github.com)

71 points by frabonacci 1 month ago | 8 comments

QEMU with VirtIO GPU Vulkan Support(gist.github.com)

224 points by GalaxySnail 1 year ago | 47 comments

Nvidia adds native Python support to CUDA(thenewstack.io)

396 points by apples2apples 1 year ago | 158 comments

The New Stack article reporting that NVIDIA has added native Python support to its CUDA GPU software toolkit.

Nvidia announces native GPU programming in Rust(developer.nvidia.com)

898 points by nonmaskable 1 month ago | 354 comments

NVIDIA Technical Blog post introducing CUDA Rust, describing two approaches for writing GPU kernels natively in Rust.

CUDA-oxide: Nvidia's official Rust to CUDA compiler(nvlabs.github.io)

390 points by adamnemecek 5 months ago | 110 comments

Documentation book for cuda-oxide, an experimental NVIDIA Labs Rust-to-CUDA compiler for writing SIMT GPU kernels in Rust.

Gemma 3 QAT Models: Bringing AI to Consumer GPUs(developers.googleblog.com)

571 points by emrah 1 year ago | 259 comments

Google Developers Blog post announcing Gemma 3 models trained with quantization-aware training so they can run on consumer GPUs.

What every developer should know about GPU computing(codeconfessions.substack.com)

422 points by Anon84 3 years ago | 149 comments

A Substack primer by Abhinav Upadhyay explaining GPU architecture and computing concepts for software developers familiar mainly with CPUs.

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.