GPUs for Startups | Vast.ai

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.

GPU Pricing — Live Platform Rates | Vast.ai

Live GPU pricing on Vast.ai. Prices set by supply and demand across 40+ data centers. On-demand, interruptible, or reserved — find the right GPU at the right price.

SF Compute: Commoditizing Compute to solve the GPU Bubble forever

Selling GPUs to avoid bankruptcy, empowering researchers with short term clusters, and why CoreWeave is maybe a real estate business

Lilypad-Tech/lilypad

Run AI workloads easily in a decentralized GPU network. https://www.youtube.com/watch?v=yQnB2Yxia4Y

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.

Rohan Paul on Twitter / X

Google is trying to win AI by making compute cheap, not by beating Nvidia on raw speed.Nvidia sells GPUs to clouds with a big 70%+ margin that sits on top of manufacturing and R&D cost and raises cloud prices.Google builds TPUs for itself at near manufacturing cost, adds no… https://t.co/aSgWRf0HY7 pic.twitter.com/T3Fzc6czwg— Rohan Paul (@rohanpaul_ai) November 25, 2025

AI GPUs probably live longer than three years

People who think current AI use is unsustainable often rely on the claim that inference GPUs only last “three years at the most” under load1. The idea here is that once the AI bubble money drains away, current infrastructure will rapidly become obsolete, and there won’t be enough money floating around to buy a whole slate of brand-new GPUs. Inference costs would thus rapidly become way too expensive for current AI products to make any financial sense.

exabox preorder

This is a fully refundable preorder for an exabox and will be credit on the purchase price. The full purchase price will be close to $10M, so do not buy if that's not your budget for AI compute! The details of the product aren't fully finalized yet, but the basics are this.It's a 20ft shipping container that needs a megawatt of power, 208V or 415V three phase (so 208-240 line). It will be self contained for cooling and weatherproof. Supported temperature and humidity ranges TBD.Like tinyboxes, it will come in both red and green. After you place a preorder, we can discuss what specific GPUs you want. The price will be under $10M and it will have about an exaflop of compute. And just like tinybox, it's 100% ready to run with tinygrad, PyTorch and others. At launch, it should be the absolute best bang for your buck at the price point with respect to FLOPS/$, GB/$ and GB/s/$.The whole box will be connected at at least 400 Gbps and is capable of training as one unit. At 50% MFU, it can do 3e24 (Kimi sized) training runs in 10 weeks. With tinygrad software, it will function as one big GPU, but it is made up of normal computers and you can also use PyTorch.This is ~1% of the purchase price and guarantees your spot in line. We should ship our first exabox Q2 or Q3 of 2027.

Pricing | Runpod

GPU cloud computing at up to 80% less than hyperscalers. Explore pricing for on-demand Pods, Serverless, Clusters, and Network Storage.

Portable Computer: Local-First AI

Run Perplexity Computer locally on NVIDIA DGX Spark. Private, on-device work with cloud escalation when needed.

NVIDIA DGX Station for Windows Puts a Trillion-Parameter AI Supercomputer on Every Enterprise Desk

News Summary: NVIDIA announces DGX Station for Windows — the world’s most powerful deskside AI supercomputer for developing and running agents on Windows — built on the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip, coming in Q4 this year. DGX Station brings frontier AI agents to Windows — enabling enterprise developers, researchers, engineers, designers and data scientists to build and deploy AI across the workflows and applications their business runs on. DGX Station will support NVIDIA OpenShell on Windows, built on new Windows security and containment primitives. TAIPEI, Taiwan, June 01, 2026 (GLOBE NEWSWIRE) - NVIDIA GTC Taipei - NVIDIA today announced NVIDIA DGX Station™ for Windows , the world’s most powerful deskside AI supercomputer designed to build, run and connect always-on AI agents to Windows applications and workflows, capable of running frontier AI models of up to 1 trillion parameters locally. Historically, heavy-duty enterprise AI workloads —

google-ai-edge/LiteRT

LiteRT, successor to TensorFlow Lite. is Google's On-device framework for high-performance ML & GenAI deployment on edge platforms, via efficient conversion, runtime, and optimization

Advancing AI Infrastructure for Agentic AI with NVIDIA DOCA In-Silicon Security | NVIDIA Technical Blog

The AI era is driving a new class of infrastructure: AI factories that transform data into intelligence for autonomous AI agents operating at unprecedented scale. Powered by accelerated computing…

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.

Benchmarking Tesla GPUs - esologic

Decommissioned enterprise tesla GPUs are widely available and extremely cheap. In this post, I benchmark tesla GPUs to see if they are useful