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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 Google Is A Full Stack AI Player, And Is Playing Well 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 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
AI-Driven CPU Shortage Saves Intel’s Financial Cookies
Intel Corporation · 2026-04-28 · via The Next Platform: In-depth coverage of high end computing

If you have a few pallets of datacenter CPUs sitting in a barn somewhere, and they have a reasonable number of cores and a healthy number of I/O lanes and memory channels, then let me tell you, you can find a buyer for them lickety-split. Thanks to the GenAI boom, Intel and AMD are focusing on putting out high core count parts because that is what high-end AI training and inference machines need.

More importantly for Intel, as we move from a market focused on AI training to one concerned overwhelmingly with AI inference in production, the ratio of GPUs or XPUs to CPUs is actually decreasing, according to Intel, not increasing. And that means CPU content per AI system is on the rise at the same time that the number of AI systems (and the aggregate amount of investment in them) is also on the rise. It’s a “double thank you mammy” instead of the double whammy bammy that Intel has lived through in the past few years when its foundry slipped some process cogs and therefore its server CPU designs fell behind rival X86 CPU supplier AMD.

Instead of the 8 to 1 ratio that was common a few years back, we see more and more 4 to 1 designs and even some 2 to 1 designs. We do not think it will go all the way down to 1 to 1, but stranger things have happened. The funny bit is that Intel CPUs are still being used in a lot of 8 to 1 and 4 to 1 designs as far as we can tell, but when Intel finally gets NVLink ports put on future Xeons, they should be a drop-in replacement in whatever rack designs Nvidia and its partners put together, which are based on 2 to 1 designs. This is a socket statement, not a chip or chiplet statement. Many times, if you drill down into the architecture, you see multichiplet CPUs and multichiplet GPUs paired into something like a 1 to 1 ratio, as we have discussed in the past.

Couple that with the fact that all of the X86 processors from Intel and AMD that can be made in 2026 seem to be allocated to their respective hyperscalers, cloud builders, OEMs, and other channel partners. How much under capacity there is remains a bit of a mystery, but Dave Zinsner, Intel’s chief technology officer, gave a hint late last week on a call with Wall Street analysts going over the chip maker’s Q1 2026 results. When one analyst asked that if Intel’s manufacturing capacity for chips (by which we presume he meant both PC and server chips) was 10 percent lower than demand, Zinsner said: “I probably not want to put a specific number. Let's just say it starts with a B. So it's meaningful.”

OK, at least $1 billion. If all of that undercapacity came from the datacenter side of the Intel Products group, then that would represent an incremental 20 percent of revenue. If it is across all Intel Products, then it is around 8 percent. If it was more than that – billions – then take it from there. It looks to us that the guess of Timothy Arcuri, semiconductor analyst at UBS Investment Bank, was pretty close at around 10 percent. We have no way of knowing if the datacenter CPUs have a bigger supply-demand imbalance than the PC CPUs. Our hunch is that the tech titans are a little more hungry for datacenter CPUs than PC and laptop makers.

Zinsner tossed out another interesting tidbit on the call with Wall Street, saying that Intel’s “collective AI-driven businesses now represent 60 percent of revenue and grew 40 percent year-over-year.” Zinsner then added that Intel’s “AI PC revenue grew 8% sequentially and now represents greater than 60 percent of our client CPU mix.” If you play around with those numbers, it means Xeon sales into AI systems probably represented somewhere around 57 percent of the total $5.05 billion in sales for the Data Center and AI group, which works out to $2.88 billion, while AI related CPUs accounted for $4.79 billion, or 62 percent of the $7.73 billion in sales for the Client Computing group.

Aside from the yields on the mature Intel 7, Intel 4, and Intel 3 processes running ahead of schedule, and ditto for the much more important Intel 18A and Intel 14A processes that are the foundation of Intel’s own CPU business as well as its merchant foundry aspirations, most of the talk on the call with Wall Street was one of relief.

People keep saying that Intel is back. No it isn’t, no more than the IBM that had a near-death experience in the early 1990s and nearly went bankrupt and laid off half of its 400,000-strong workforce “was back.” Intel is not back. Intel is transformed, trimmed down, and focused on doing what it can to delight customers. And it will be lucky to have a 25 percent share of the overall CPU business and some low double digit share of the merchant foundry business in the coming years. Yes, Intel has plenty of advantages when it come to advanced packaging. I know. So did IBM Microelectronics, which also had several generations of Power cores that could do twice as much work as an X86 core as well as advanced packaging. And then IBM’s chip business disappeared into the gaping maw of GlobalFoundries when the foundry game became too rich for Big Blue’s blood. IBM basically paid GlobalFoundries to take it.

What is emerging here in 2026 is a less cocky, more focused, and absolutely realistic Intel. It is not the company run by marketeers that read the pricing and technical riot act to hyperscalers and cloud builders in the 2010s, and essentially pushed these key customers into the awaiting arms of Arm. It is not the paranoid one that nearly crashed and burned as it exited the memory business so many decades ago to foster an X86 CPU business and make it grow from the desktop to the laptop to the datacenter. This is a different Intel, and Lip-Bu Tan is setting the tone and calling the conservative shots. Like IBM under Lou Gerstner, Intel will only invest in technologies that is reasonably sure will make money. Which is why the 14A process node is not a sure thing yet. Intel cannot – and will not – carry the financial burden of ramping 14A all by itself. Tan has been very clear on this, and he shows no indication of changing his mind.

As we said, thanks to product mix and what we presume are aggressive pricing tactics when demand exceeds supply, Intel’s DCAI group had revenues of $5.05 billion, up 22.4 percent year on year and up 6.6 percent sequentially. More important, due to yield improvements as well, operating income rose by a very nice 2.7X to $1.54 billion. Some of that improved profit comes from cutting jobs, too, which Intel has certainly done, and all of these things could have been done by former Intel chief executive officer Pat Gelsinger. But, just as was the case with John Akers, an insider who ran IBM up on the rocks and who couldn’t or wouldn’t make the tough choices, someone else, in this case former American Express CEO Lou Gerstner, had to come in and swing the double-headed axe, cutting people and projects until revenues and costs came back into something akin to balance.

That operating income for DCAI as a share of profits hit 30.5 percent, and we think it can grow from there. Only three years ago, DCAI essentially had no profits, and a decade and a half ago it had operating profits in the 50 percent of revenue range.

As another sign of strength, Intel bought the 49 percent minority stake it had sold in its Fab 34 foundry in Ireland to private equity firm Apollo Management. Intel sold that stake for $11.2 billion, and in early April paid Apollo $14.2 billion to get it back. So it was a temporary loan with a $3 billion fee for ten months. The US government has invested $11.1 billion in total into Intel, and Nvidia has invested another $5 billion, so that is the roundabout way that Intel could afford to do this. Intel exited the quarter with $17.3 billion in cash and equivalents, another $15.5 billion in short-term investments, and another $8.5 billion in equity investments. So it has maneuvering room.

But not enough to make a big mistake. Or maybe even a few little ones.