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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 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 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
Broadcom May Become The Biggest Counterbalance To Nvidia
Timothy Prickett Morgan · 2026-03-06 · via The Next Platform: In-depth coverage of high end computing

Here’s a funny thought. If Broadcom had not bought legacy software maker Computer Associates in November 2018 for $18.9 billion, it would have had a harder time buying server virtualization juggernaut VMware for $69 billion in November 2023. And without the enormous profits it gets from both of these legacy software providers, it would have been hard pressed to have the patience to build a custom AI XPU business that will soon dominate its vast chip business.

The fact that chief executive officer Hock Tan, who was running Avago Technologies when he took a shining to chip maker Broadcom, came up with the $37 billion it took to do the Broadcom deal in May 2015 in the first place to even do that monster deal has made the modern Broadcom as we know it today even possible.

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This has been no mean feat.

Building conglomerates rarely works out the way Wall Street or the top brass at a company hopes. Sure, the bankers do fine collecting their fees for putting things together and then collect more fees a decade later ripping them apart “to unlock shareholder value” when it doesn’t work out. But in a complex company with many product lines, there is always something going wrong even when other things are going right. Every once in a while there is a harmonic convergence where everything is going right, say IBM circa 1989, and everybody thinks it will last forever. But then along comes the cacophonous divergence when everything goes wrong, like IBM circa 1992. There is much weeping and gnashing of teeth, and in the case of IBM, a massive layoff of half of its 400,000-strong global workforce and the largest writeoffs in the history of business.

Well, so far anyway. If this AI bubble bursts, we will see some much bigger writeoffs, but perhaps not so many layoffs. The layoffs come if AI succeeds, not it if it just grows instead of maintaining an exponential. So far, this AI bubble is made of Super Elastic Bubble Plastic, and if you are of a certain age you know what I mean.

Anyway, having one or two of the cylinders sticking all the time is why IBM has struggled over its most recent decades, and it is also why Hewlett Packard Enterprise and Dell had their own woes in the 2000s as they tried to build Baby Blues of their own, mixing datacenter hardware, software, and services.

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But thus far, Avago transformed into the modern Broadcom has worked – and worked marvelously.

Tan is legendary for stripping out unnecessary costs and tuning up businesses to kick out profits and has made this hybrid hardware-software company work. And Tan has steered Broadcom brilliantly to make the most of an AI boom where the hyperscalers, cloud builders, and now the AI model builders want to control their own compute engine fates and also want the very best networking they can coax Broadcom into designing.

The AI compute and networking business utterly dominates the Nvidia business, and it is not quite there yet for Broadcom. But if current trends persist in homegrown XPUs for AI workloads, it is not hard to imagine that the combination of Broadcom’s custom ASIC business, its storage and networking chips, and its emerging rackscale systems business will pack a bigger wallop than even AMD does today. AMD may rue the day it spun off its ZT Systems business when Broadcom really starts humming along, and Supermicro, Lenovo, Dell, Foxconn, Quanta Computer, and Inventec may also wish that Tan just stuck with chips and stayed out of racks.

This all sounds weird, I know. But all it takes is for AI inference to become highly tuned at a few companies on custom AI hardware in a mad dash to drive down the cost of chewing on and spitting out tokens for Broadcom (and to a lesser extent, Marvell) to take big bites out of the GPU and networking businesses at Nvidia and AMD. Broadcom clearly wants to cut out the OEMs and ODMs and just do it all, and customers may want one throat to choke for the whole shebang.

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With that, let’s get into Broadcom’s Q1 F2026 financial results.

In the quarter ended on February 1, Broadcom posted $19.31 billion in sales, up 29.5 percent, with operating income up 36.8 percent to $8.56 billion and net income rising almost as fast (up 33.5 percent) to $7.35 billion. That net income represents 38.1 percent of revenues, and clearly that legacy software is cushioning some of the blow of building custom AI chips for hyperscalers and model builders – an inherently less profitable business that Tan & Co cannot walk away from.

Broadcom ended the quarter with $14.17 billion in cash, and its cash pile is generally trending upwards. But at the same time, the debts it amassed buying the original Broadcom and VMware are still hanging around, with the company owing $66.1 billion in various debts.

The company’s chip group, called Semiconductor Solutions, had a 54.2 percent revenue pop in Q1 F2026 to $12.52 billion, and AI chippery was the big driver here. (More on that in a moment.) Operating incomes for Semiconductor Solutions rose by a very nice 60.4 percent to $7.51 billion, which represented 60 percent of revenues. If margins are not particularly high on AI accelerators and probably even lower on the AI rackscale systems that Broadcom is now ramping up for one of its six AI infrastructure customers, then they must be pretty damned good on a whole bunch of other things.

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The Infrastructure Software group, which includes the CA mainframe and Unix system software, the Symantec PC and server security stack, and the VMware server virtualization stack, had $6.8 billion in sales, up a mere 1.4 percent year on year. Broadcom has not given out specific VMware figures since Q3 F2024, but this time around said VMware sales were up 13 percent to what we think is around $5.2 billion in our model. Tan said further that VMware had a revenue backlog of $9.2 billion, which is almost triple what it was a year ago.

Operating income for the Infrastructure Software group, thanks to the Tan Squeeze, rose by 4 percent to $5.3 billion. Operating margins for the Broadcom software business have been rising since the VMware hit as it was absorbed into the conglomerate, but it seems to have topped out at 78 percent of revenue. (That’s not so bad. The operating income at IBM’s absolutely captive software business, where customers have no alternatives whatsoever except in some cases from Broadcom, are around 88 percent, which is an industry peak that very few companies are ever going to match, much less beat.)

In any event, that Broadcom software business contributed more than its share to the middle line.

Now, let’s talk about Broadcom’s divisions.

Up until five quarters ago, Broadcom gave out financial breakdowns for its various semiconductor divisions. But with AI on the rise, it has stopped being specific about them and prefers now to talk about AI chips (and systems) versus other chips. It gives a few hints here and there, and we have kept our model going, admittedly with some error bars around the data.

We think that the Networking division (which includes AI stuff, including XPUs and rackscale systems) had sales of $7.91 billion, up 74.9 percent. Server Storage Connectivity – all that other datacenter chippery for peripherals – had maybe $1.1 billion in sales, up 13.3 percent, while chips on the Wireless division had maybe just under $2 billion in sales, off a point and a half. In our model, the Broadband division had $1.37 billion in sales, up 2.5X, and yes, that sounds a little high to us, too.

The way that Broadcom wants you to think about its chip business is AI versus non-AI, and here is what that split looks like:

What I think we are seeing in that orange line above is increasing competition in networking from Nvidia and Cisco Systems for both hyperscale Web and analytics infrastructure and for AI systems, too, as well as increasing competition from Astera Labs for PCI-Express switching and from Marvell on a whole slew of peripheral chippery.

Here is the data behind that chart above, just so you have it:

As is our custom, items shown in bold red italics are estimates from The Next Platform.

In our model, AI chip (and system) revenues rose by more than 2X to $8.44 billion in Q1 F2026. About a third of that was driven by AI networking chips and the other two thirds was driven by AI accelerators and rackscale systems. If you do the math, that is $5.65 billion in AI chips and systems, up 2.4X year on year and up 7.4X sequentially compared to a pretty weak Q4 F2025. AI networking drove $2.78 billion, up 60 percent year on year, but down 51.5 percent sequentially.

If I had to guess, Broadcom had some big customers do a lot of AI networking chip buying in Q4 F2025 precisely so it would counterbalance low AI XPU sales as its customers transition to new generations of compute engines throughout this year. That’s what I would do if I was a hyperscaler, cloud builder, or model builder. Get the network done so the AI systems can just roll in and plug in.

Broadcom has built relationships with five big AI customers for hone-designed XPUs, and now has added a sixth who will be ramping this year. Tan said that demand from Google for its “Ironwood” TPU v7 accelerators would be strong in 2026 and in 2027 and beyond demand for TPUs from Google would be even stronger. Separate from Google’s own TPU demand, Broadcom is also benefitting from Anthropic’s decision to buy $10 billion in TPU racks, with Google’s permission. The plan is for Anthropic to install 1 gigawatt of TPU v7 capacity in 2026 (we presume Tan is speaking in fiscal years) and that this will more than triple to in excess of 3 gigawatts in 2027 (again, a fiscal year).

Tan added that in contrast to what many rumors say about the MTIA accelerator effort at Meta Platforms, “Meta’s custom accelerator MTIA roadmap is alive and well” and it is shipping now. We presume this refers to the MTIA v2 that was revealed in April 2024. Tan added that for the next generation MTIA v3, which has not been revealed as yet, Meta Platforms will scale production to “multiple gigawatts” of compute capacity.

For the fourth and fifth AI customers, Tan told Wall Street that these customers would have “strong shipments” this year and more than double in fiscal 2027. The rumors are that AI custom ASIC customer 4 is ByteDance and customer 4 is Apple. Customer six is, of course, OpenAI, which is using Broadcom to shepherd its “Titan” AI XPU through manufacturing and assembly. Tan said that OpenAI would install over 1 gigawatt of Titan capacity in fiscal 2027.

Here is how Tan sees the lay of the land right now:

“Let me take a second to emphasize our collaboration with these six customers to develop AI XPUs is deep, strategic, and multiyear. We bring to the partnerships, each of them, unmatched technology in SerDes, silicon design, process technology, advanced packaging and networking to enable each of these customers to achieve optimal performance for their differentiated LLM workloads. We have the track record to deliver these XPUs and high volumes at an accelerated time to market with very high yields.”

“And beyond technology, we provide multiyear supply agreements as our customers scale-up deployment of their compute infrastructure. Our ability to assure supply in these times of constrained capacity in leading-edge wafers, in high-bandwidth memory and substrates ensures the durability of our partnerships, and we have fully secured capacity of these components for 2026 through 2028.”

Tan said that Broadcom’s visibility into 2027 for these customers had improved so much that he could draw a $100 billion line in the sand for a minimum for overall AI revenues for that fiscal year. (Later in the call, Tan said it would be “significantly in excess of $100 billion” as a target.)

To put that into perspective, back in fiscal 2022, Broadcom’s AI revenues were $1.93 billion, and grew in fiscal 2023 to $3.81 billion. In fiscal 2024, they jumped by more than 3X to $12.74 billion, and hit $20.25 billion in fiscal 2025. So assume around a 2.5X growth factor for AI revenue in fiscal 2026, and a 2X again in fiscal 2027 and you get to $100 billion. And we think it will probably more than double in 2028, which is an even bigger year for AI system deployments for a lot of different reasons – provided all the money does not run out.

More immediately, looking ahead to Q2 F2026, Broadcom is expecting for sales of around $22 billion, up 47 percent year on year. The expectation is for Semiconductor Solutions to see 76 percent growth year on year to $14.8 billion, with AI chips and systems growing by 2.4X again to $10.7 billion. Infrastructure Software revenues will rise by 9 percent to $7.2 billion, which means more profits in the coffer to invest in the AI buildout.