NVIDIA Linux open GPU with P2P support
eGPUs on NixOS
Getting an Nvidia eGPU working on NixOS: Thunderbolt authorization, kernel/module setup, and fixing Gamescope glitches with the latest beta driver.
GPU Support [Open Beta] - Polars user guide
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.
egpu for mac - tinygrad docs
TinyGPU app lets you use AMD and NVIDIA GPUs on macOS over USB4/Thunderbolt with tinygrad.
Installing the NVIDIA Container Toolkit — NVIDIA Container Toolkit
Install the NVIDIA GPU driver for your Linux distribution. NVIDIA recommends installing the driver by using the package manager for your distribution. For information about installing the driver with a package manager, refer to the NVIDIA Driver Installation Quickstart Guide. Alternatively, you can install the driver by downloading a .run installer.
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.
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
Setting up a virtual machine with GPU passthrough
A guide for configuring NixOS with nvidia GPU passthrough to a virtual machine in virt-manager.
NVIDIA GPUs Work on macOS Again. The Driver Is a Miracle. The Inference Is Not.
We benchmarked an RTX 3090 over USB4 and profiled every kernel. GPUs use 1.2-1.6% of their memory bandwidth. The bottleneck is the compiler, not the cable.
NVIDIA NIM for Developers
NVIDIA NIM is a set of accelerated inference microservices that allow organizations to run AI models on NVIDIA GPUs anywhere—in the cloud, data center, workstations, and PCs.
clem 🤗 on Twitter / X
Introducing Kernels on the Hugging Face Hub ✨What if shipping a GPU kernel was as easy as pushing a model?- Pre-compiled for your exact GPU, PyTorch & OS- Multiple kernel versions coexist in one process- torch.compile compatible- 1.7x–2.5x speedups over PyTorch baselines pic.twitter.com/U0qDdxCWkd— clem 🤗 (@ClementDelangue) April 14, 2026
Run Ollama with NVIDIA GPU in Proxmox VMs and LXC containers
Learn how to run Ollama with an NVIDIA GPU in Proxmox for an enhanced AI experience in your home lab and great chat performance
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.
NSF and NVIDIA award Ai2 a combined $152M to support building a national level fully open AI ecosystem | Ai2
Ai2 has been awarded a combined $152 million from the U.S. National Science Foundation (NSF) and NVIDIA as part of a jointly funded project to advance our research and develop truly open AI models and solutions that will accelerate scientific discovery.
HipScript: Run HIP and CUDA code with WebGPU
Online compiler for HIP and NVIDIA® CUDA® code to WebGPU
NVIDIA Shatters MoE AI Performance Records With a Massive 10x Leap on GB200 'Blackwell' NVL72 Servers, Fueled by Co-Design Breakthroughs
Scaling performance on MoE AI models is one of the industry constraints, but it appears that NVIDIA has managed to make a breakthrough.
