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.
Pricing | Runpod
GPU cloud computing at up to 80% less than hyperscalers. Explore pricing for on-demand Pods, Serverless, Clusters, and Network Storage.
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.
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Discover the most efficient way to build, tune and run your AI models and applications on top-notch NVIDIA® GPUs.
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
TPUs vs. GPUs and why Google is positioned to win AI race in the long term | Hacker News
To quote The Next Platform: "An Ironwood cluster linked with Google’s absolutely unique optical circuit switch interconnect can bring to bear 9,216 Ironwood TPUs with a combined 1.77 PB of HBM memory... This makes a rackscale Nvidia system based on 144 “Blackwell” GPU chiplets with an aggregate of 20.7 TB of HBM memory look like a joke."
Fastino trains AI models on cheap gaming GPUs and just raised $17.5M led by Khosla | TechCrunch
Tech giants like to boast about trillion-parameter AI models that require massive and expensive GPU clusters. But Fastino is taking a different approach.
Nvidia invests $5 billion into Intel to jointly develop PC and data center chips
Intel will help build x86 chips with Nvidia RTX GPU chiplets
Why compute might get 10x more expensive in coming years
If a human-level software engineer that could run on an H100 equivalent, at current market rates for software engineers, that H100 should rent for over $250k a year. That’s 15x today’s spot price.
Lilypad-Tech/lilypad
Run AI workloads easily in a decentralized GPU network. https://www.youtube.com/watch?v=yQnB2Yxia4Y
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.
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.
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 —
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