Rohan Paul on Twitter / X

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."

Expanding our use of Google Cloud TPUs and Services

Announcing a dramatic increase in Anthropic's compute resources

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.

GPUs for Startups | Vast.ai

Affordable, Scalable Compute for Startups

We're launching two specialized TPUs for the agentic era.

The eighth generation of Google’s TPU includes two specialized chips that will power the future of AI.

Google Cloud Next 2026: The End Of The AI Pilot Era

Google Cloud Next 2026 opened with Thomas Kurian declaring the end of the AI pilot era and Sundar Pichai comparing the enterprise refrain of last year (“Can we build an agent?”) to today’s: “How do we manage thousands of them?” Google Cloud Next ’26 answers the second question with a single product story: Gemini Enterprise […]

TPU architecture  |  Google Cloud Documentation

Tensor Processing Units (TPUs) are application specific integrated circuits (ASICs) designed by Google to accelerate machine learning workloads. Cloud TPU is a Google Cloud service that makes TPUs available as a scalable resource.

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.

Anthropic commits to spending $200 billion on Google's cloud and chips: Report

The commitment suggests the AI startup accounts for more than 40 per cent of the revenue backlog Google disclosed to investors last week, according ‌to the report.

Understanding TPUs vs GPUs in AI: A Comprehensive Guide

Explore the differences between Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) in 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.

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.

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…

The 2026 Developer's Guide to Free Google Cloud Credits (For AI & Side Projects)

If you’re a beginner or developer who wants to pursue a career in AI in 2026, you can’t ignore the...

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