The Python-to-native compiler for AI. We remove everything between your model and the GPU.
Import AI 470: No rights for machines; automating environment generation with SPADE; and building better GPU kernels with Hawkeye
Differential acceleration of cyber, math, and AI
mudler/LocalAI
LocalAI is the open-source AI engine. Run any model - LLMs, vision, voice, image, video - on any hardware. No GPU required.
The Universal Execution Layer for AI
Optimize any AI model on any engine, across all hardware. Dria’s topology-aware compiler and peer-to-peer runtime merge CPUs, GPUs, NPUs & chiplets into one fabric—maximising utilisation, cutting inference cost and ending vendor lock-in.
Novita AI – Model Libraries & GPU Cloud - Deploy, Scale & Innovate
Novita AI provides 200+ Model APIs, custom deployment, GPU Instances, and Serverless GPUs. Scale AI, optimize performance, and innovate with ease and efficiency.
Mesh LLM: distributed AI computing on iroh
How Mesh LLM pools existing GPU resources across machines into a single OpenAI-compatible API, built on iroh.
TheStage AI – Faster, Cheaper AI Inference
Accelerate models on NVIDIA & edge. Full guides for setup, optimization & deploy. ANNA, QLIP, Elastic Models, CLI & API. Built for AI teams & devs.
Locally AI - Run AI models locally on your iPhone, iPad, and Mac.
Run Llama, Gemma, Qwen, DeepSeek, and more on your iPhone, iPad, and Mac. Optimized for Apple Silicon. Offline. Private.
Where AI Startups Scale to Production
Discover the most efficient way to build, tune and run your AI models and applications on top-notch NVIDIA® GPUs.
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.
Building with Open Models
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.
Im going back to writing code by hand
I vibe-coded a GPU-aware Kubernetes TUI for 7 months, archived it, and started over. Here's what AI gets wrong when projects grow complex.
Together AI | The AI Native Cloud
Build what's next on the AI Native Cloud. Full-stack AI platform for inference, fine-tuning, and GPU clusters — powered by cutting-edge research.
Import AI 464: Fables writes GPU kernels; AI automation; and analog computation
Is this the beginning of a new world?
