Polars provides an in-memory, GPU-accelerated execution engine for Python users of the Lazy API on NVIDIA GPUs using RAPIDS cuDF. This functionality is available in Open Beta and is undergoing rapid development.
RightNow AI - YC-Backed GPU Research Lab
YC-backed GPU research lab building the RightNow CUDA editor, RunInfra inference infra, Forge kernels, and publishing AutoMegaKernel and related papers on arXiv.
HipScript: Run HIP and CUDA code with WebGPU
Online compiler for HIP and NVIDIA® CUDA® code to WebGPU
TypeGPU – Type-safe WebGPU toolkit
Discover how it can change the way you work with GPU rendering and computing.
FOSDEM 2022 - LibVF.IO: vGPU & SR-IOV on Consumer GPUs using Nim
I'd like to showcase LibVF.IO's new LIME Runtime feature (Lime Is Mediated Emulation) and do a deep dive on open source vGPU technology in general.
Polar — A billing platform for the intelligence era | Polar
Polar is the Merchant of Record for developers building AI-era software: payments, subscriptions, and usage-based billing, with global tax handled for you.
Christian Legnitto Interview, Maintainer: rust-gpu, rust-cuda [Rust Project Content @ RustConf 2025]
Iwo Plaza – Your GPU is a JavaScript runtime* (TypeGPU deep-dive)
Chris Lattner on Twitter / X
Please don’t tell anyone: we aren’t just open sourcing all the models. We are doing the unspeakable: open sourcing all the gpu kernels too. Making them run on multivendor consumer hardware, and opening the door to folks who can beat our work.Plz keep it quiet, ok? 😉— Chris Lattner (@clattner_llvm) March 24, 2026
ASUS Ascent GX10
Desktop AI supercomputer delivering up to 1 petaFLOP performance, powered by NVIDIA GB10 Grace Blackwell Superchip, supporting OpenClaw and Hermes Agent.
software-mansion/TypeGPU
A modular and open-ended toolkit for WebGPU, with advanced type inference and the ability to write shaders in TypeScript
Modern GPU Programming For MLSys — Modern GPU Programming For MLSys
Machine learning systems sit at the heart of modern AI workloads. In these systems, performance often comes down to the quality of a small number of GPU kernels. Attention kernels, LLM prefill and decode kernels, low-precision block-scaled GEMMs, fused MoE layers, and other large fused kernels all directly shape end-to-end speed in both training and serving.
Modern GPU Programming For MLSys — Modern GPU Programming For MLSys
Machine learning systems sit at the heart of modern AI workloads. In these systems, performance often comes down to the quality of a small number of GPU kernels. Attention kernels, LLM prefill and decode kernels, low-precision block-scaled GEMMs, fused MoE layers, and other large fused kernels all directly shape end-to-end speed in both training and serving.
Building with Open Models