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The Next Platform: In-depth coverage of high end computing

Uncle Sam Awards $2 Billion-Plus To Quantum Companies, But Wants A Cut Oak Ridge Starts Weaving Together A Quantum, Classical HPC, And AI System Stack Dell Bulks Up Hardware As AI Infrastructure Shifts To On-Premises Cisco Wins Over AI Customers With Merchant Silicon And Optics With Its IPO Done, Cerebras Can Get Back To Pushing The AI Envelope HPE Throws VM Users A Lifeline, Unifying Containers And VM Management In Cloud Stack OpenAI, Microsoft And Friends Build A Better, More Scalable Ethernet Compute And Memory Price Hikes Drive IT Spending Way Higher Sometimes, Air Is The Only Way For AI Systems To Keep Their Cool Arista Rides AI Scale Out Networks, Moves Into Scale Across, And Awaits Scale Up If You Can Make A Compute Engine, You Can Sell A Compute Engine Cleveland Clinic Simulates Large Proteins With Quantum-Centric Supercomputing Broadcom Helps CPU And XPU Makers Go Vertical With Compute Microsoft Committed To Doubling AI Infrastructure In Two Years AWS Will Be An OEM, Just Like Google And Maybe Microsoft New Google Networks Tuned Up For GenAI Inference And Training Microsoft And OpenAI Remain Friends, Are Looking To Hook Up With Others AI-Driven CPU Shortage Saves Intel’s Financial Cookies The GenAI Battle Shifts From Frontier Models To Agentic Platforms With TPU 8, Google Makes GenAI Systems Much Better, Not Just Bigger Cisco Scales Out Quantum Systems With A Quantum Network Switch The Second Time Will Be The IPO Charm For Cerebras Imagine An Army Of AI Minions Handling Incident Response AI Will Soon Drive A Third Of TSMC’s Business Bechtolsheim & Friends Breathe Life Into Pluggable Optics One Last Time How HPC And AI Digital Twins Accelerate Quantum Error Correction The Embrace Of AI In Design Transforms Cadence And Its Customers Nvidia Brings The Power Of Open Source AI Models To Quantum Computing Building The Imperfect Beast For Enterprises, GPUs Need Virtualization As Much As CPUs Ever Did CoreWeave Takes As Much Financial Engineering As It Does Datacenter Design Contemplating Meta’s Homegrown MTIA Compute Engine Roadmap Most Neoclouds, Sovereigns, And Enterprises Will Buy, Not Build, Their AI Stacks Broadcom And Google Benefit Mightily From Anthropic’s Meteoric Growth Rebellions AI Rings Up The Money To Rack Up AI Inference Systems Nvidia Software Pushes MLPerf Inference Benchmarks To New Highs Broadcom Makes Its Pitch To Run Kubernetes On VMware VCF The $2 Billion Nvidia Deal With Marvell Is About A Lot More Than NVLink Fusion Classiq Says Quantum Is On Its Way, But Patience Is Needed Demonstrating The Scientific Usefulness Of Quantum Systems We Need Servers – Lots Of Servers. . . . Arm Comes Full Circle With Homegrown, AI-Tuned Server CPU Riding The Memory Boom And Trying To Avoid The Bust Data Analytics Helps Make The Mighty Lionesses Roar Driving Down The AI System Roadmap With Nvidia The Open Agentic AI World According To Nvidia Nvidia Finally Admits Why It Shelled Out $20 Billion For Groq Nvidia Says OpenClaw Is To Agentic AI What GPT Was To Chattybots IBM Unrolls Blueprint For Quantum-Classical HPC Computing Women Get Data-Driven Health Boost As The FA Tackles Sports Science Four Months Into Its Comeback, Zapata Stakes Its Claim In Quantum Software Eridu Cuts To The AI Networking Chase With High Radix Switch System HPE Works Harder And Smarter To Chase Datacenter Profits We Need A Proper AI Inference Benchmark Test How AI Is Boosting Gender Equality In High Performance Racing Custom Compute Engine Biz Growing More Than Marvell Ever Hoped Broadcom May Become The Biggest Counterbalance To Nvidia Ayar Labs Gets $500 Million To Ramp Photonics Into 2028 AI Systems With Cisco Outshift, Agentic AI Is Teed Up For the Internet Of Cognition Nvidia Sees The Light On Silicon Photonics And Maybe Optical Switching AI Servers Finally Dominate Dell’s Systems Business VAST Data: What Controls The Data Is More Important Than What Stores It So Far, Nobody Turns Tokens Into Money Like Nvidia SambaNova Pits Its Engineering Against Nvidia For Agentic AI Some More Game Theory, This Time On The AMD-Meta Platforms Deal AMD Says “Helios” Racks And MI400 Series GPUs On Track For 2H 2026 CPU-Only Compute Still Matters To A Lot Of HPC Centers Taalas Etches AI Models Onto Transistors To Rocket Boost Inference Some Game Theory On That Nvidia-Meta Platforms Partnership AI Eats The World, And Most Of Its Flash Storage The Current AI Networking Wave Will Be A Tsunami Of Money By 2027 The Memory Crunch Pinches Cisco’s Profits Only A Few AI Platforms Can Survive The Greatest AI Show On Earth Cisco Doubles Up The Switch Bandwidth To Take On AI Scale Out And Eventually Scale Up Datacenter Spending Forecast Revised Upwards – Yet Again The Twin Engine Strategy That Propels AWS Is Working Well With GenAI Turbochargers, Google Is Shifting Its Cloud Into A Higher Gear AMD Finally Makes More Money On GPUs Than CPUs In A Quarter Dassault And Nvidia Bring Industrial World Models To Physical AI TACC Explores Mixed Precision And FP64 Emulation For HPC With Horizon Robotics Will Break AI infrastructure: Here's What Comes Next Oracle’s Financing Primes The OpenAI Pump Gartner Takes Another Stab At Forecasting AI Spending Microsoft Is More Dependent On OpenAI Than The Converse Big Blue Poised To Peddle Lots Of On Premises GenAI Microsoft Takes On Other Clouds With “Braga” Maia 200 AI Compute Engines Nvidia’s $2 Billion Investment In CoreWeave Is A Drop In A $250 Billion Bucket Intel Is Still Struggling In The Datacenter, But It Could Get Better Is Nvidia Assembling The Parts For Its Next Inference Platform? TSMC Has No Choice But To Trust The Sunny AI Forecasts Of Its Customers Cerebras Inks Transformative $10 Billion Inference Deal With OpenAI By Decade’s End, AI Will Drive More Than Half Of All Chip Sales Startup Quantum Elements Brings AI, Digital Twins To Quantum Computing D-Wave Makes Gate-Model Power Move With Quantum Circuits Buy Building The Future Of Software In The AI-Native Era Arista Modular Switches Aim At Scale Across Networks, Hit Scale Out, Too NextSilicon Takes Aim At CPUs And GPUs With “Maverick-2” Dataflow Engine How HPC Is Igniting Discoveries In Dinosaur Locomotion – And Beyond Oracle First In Line For AMD “Altair” MI450 GPUs, “Helios” Racks
Google Is A Full Stack AI Player, And Is Playing Well
Timothy Prickett Morgan · 2026-05-03 · via The Next Platform: In-depth coverage of high end computing

Google might have come late to the cloud game and it might be a distant third compared to Amazon Web Services and Microsoft Azure, but Google Cloud is making up for lost time thanks to the killer app that GenAI has become. And while Nvidia is the dominant full stack player out there when it comes to hardware, which is why its revenues and profits have exploded, Nvidia’s Nemotron models are not anywhere as popular as Google Gemini, OpenAI GPT, or Anthropic Claude.

Here’s the funny bit. Google never really believed in raw infrastructure clouds. The original cloud services from Google – Compute Engine and App Engine – abstracted all of that infrastructure away to make infrastructure easier, invisible. More like the Borg and Omega platform that internal Google programmers had access to. And customers didn’t care.

But here we are, more than a decade later, and IT organizations have learned to stop hugging their physical and virtual servers and they are perfectly happy to use APIs for Google’s Gemini model as well as to train their models and run their inference against Google’s vast global fleet of GPUs and TPUs. Which is why Google Cloud is growing faster than either AWS or Azure in the first quarter. The customers have finally caught up to the Google vision, and there are customers who are choosing the full Google stack for the GenAI, from the raw iron up through data services and inference engines.

Building a cloud has not been easy for Google, and it lost a lot of money on it in the early years, as the chart above shows. But now, Google Cloud is as large as all of Google was a decade ago – and Google Cloud is more profitable by a factor of 1.25X compared to the entirety of Google was back then. It is arguable that Google is the best company in the history of IT in terms of building hyperscale infrastructure and running it efficiently, and that is showing up in the operating income for Google Cloud. It just took a bit of time for Google to get there and for IT shops to catch up to the culture that the search engine and advertising giant was peddling.

The bad news if you are a competitor of Google’s is that the search engine business and the various other advertising businesses at the company (YouTube and network advertising) are collectively doing well. Search revenues were up 19 percent to $60.4 billion, YouTube advertising was up 11 percent to $9.9 billion, while network advertising was off 4 percent to $7 billion. The subscriptions, platforms, and devices business, which includes Pixel phones, YouTube Music, Google One, and other services, rose by 19 percent to $12.4 billion. So, despite all the grief that comes as we use chattybots to do searches, these businesses are still growing, and growing faster than you might think possible.

It is not clear how many subscribers Google has for its flagship Gemini model, but the company did say that its “first party models” – meaning its own Gemini models served out as APIs, as well as Lyria, Nano Banana, Veo, and Gemma – processed an average of 16 billion tokens per minute in the March quarter, up 60 percent from 10 billion tokens per minute in the Q4 2025 quarter. By our math and what Google said about its averages, that means it processed 917.3 trillion tokens against Gemini on behalf of customers in Q3 2025, with 1,310.4 trillion tokens in Q4 2025 and 2,050.6 trillion tokens in Q1 2026.

Presumably the Google Gemini App division pays Google Cloud for the use of TPUs, just like any other customer does. (Albeit at an attractive price, I suspect.) Google does not talk about how many tokens it is generating for customers using Nvidia or AMD GPUs. But Sundar Pichai, the company’s chief executive officer, said on a call with Wall Street analysts that in the past twelve months, 330 customers on Google cloud had processed over 1 trillion tokens each, and 35 of these had broken through the 10 trillion token barrier. The use of the BigQuery tabular database in conjunction with Gemini has grown by 30X in the past year as well as companies see the benefits of a completely unified Google AI stack, from TPU up through Gemini and the Gemini Enterprise Agent Platform (an enhanced version of what was formerly known as Vertex AI) that wraps around it.

This is the driving force for the phenomenal growth that Google saw in its eponymous cloud in the first quarter, and its enormous revenue backlog of $462 billion – about half of which will be recognized within the next two years – is why Wall Street did not freak out when Google talked about spending somewhere between $180 million and $190 million this year on capital expenses. The lack of such a revenue backlog is why Meta Platforms took a shellacking when it was going to spend on that order of magnitude.

As you can see, the capex has to rise to keep pace with the backlog, which is not dominated by model builder OpenAI, whose revenue streams and operating losses are something to worry about when you are planning three, four, or five years into the future.

Google Cloud is finally hitting its stride, and I think part of the reason is that full stack integration. So does the company’s CEO.

“Google Cloud is differentiated because we are the only provider to offer first party solutions across the entire enterprise AI stack,” Pichai said on the call. “Our growth in revenue, operating margin, and backlog highlights this differentiation. Our Enterprise AI solutions have become our primary growth driver for Cloud for the first time. In Q1, revenue from products built on our gen AI models grew nearly 800 percent year over year. We are winning new customers faster, with new customer acquisition doubling compared to the same period last year. We are seeing strong deal momentum, doubling the number of $100 million to $1 billion deals year on year, and signing multiple billion dollar plus deals. And we are deepening relationships with existing customers. Customers outpaced their initial commitments by 45 percent, accelerating over last quarter.”

To put the numbers on it, Google Cloud had revenues of just a tad over $20 billion, up 63.4 percent year on year and up 13.4 percent sequentially. (That is more than double the growth of Amazon Web Services in the same Q1, but AWS is almost twice as large as Google Cloud.) If current growth rates persist, Google Cloud would catch AWS, with each having more than $82 billion in revenues by Q1 2029. Google can certainly afford to invest to underpin that level of cloud revenue. But as I said higher up in this story, the important thing is that Google Cloud is showing better profitability, with operating income up by more than a factor of 3X year on year to $6.6 billion, which was up 24.2 percent sequentially. Some prior investments in infrastructure are clearly paying off.

As Q1 2026 came to an end, Google had shelled out $35.67 billion in capital expenses, and the company said that about 60 percent was for servers and 40 percent was for networking and datacenters. This ratio has held for the past five quarters that Google has been talking about it.