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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 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
Riding The Memory Boom And Trying To Avoid The Bust
Timothy Prickett Morgan · 2026-03-25 · via The Next Platform: In-depth coverage of high end computing

The bust cycles in the memory market are horrible, but the booms are a thing to behold, and with GenAI voraciously consuming all the DRAM, HBM, and flash memory it can get allocated through the hyperscalers, cloud builders, and model builders and supplies well short of demand for the foreseeable future, there has never been a better time to sit tight and just keep waiting for the prices to rise week after week after week.

The memory makers are going to make a fortune, and all systems, whether they are for traditional workloads or for GenAI models, are going to get a lot more expensive. Given this, and the long history of boom-bust cycles in the memory market, the DRAM and flash makers have very little incentive to accelerate their capacity buildouts and every incentive to stick to their plans and just watch the profits swell as demand is, what, 2X to 3X higher than industry capacity, which is growing at maybe 20 percent to 30 percent a year.

This is going to drive system architects mad.

They are going to have to figure out ways to get more work done with less memory. As impossible as that may seem given the need for vector databases and KV cache servers in a new intermediate tier. Given this, those software-defined storage arrays that can do more with less DRAM and flash capacity are going to be the winners. In-memory data compression and de-duplication, other ways of efficiently encoding and retrieving data, and techniques to bundle up data and hit flash as little as possible (to keep it from wearing out) would seem to be important, just to give a few examples. These metrics will be as important as cost per GB and IOPS read and write because it will not be enough to buy memory and flash – it has to be used maximally. Just like compute in the GenAI, given its enormous expense. We are not far away from a day when a GPU accelerator will cost $100,000 a pop. (About six months)

OK, so maybe this is a system architect’s dream. . . .

The top brass at Micron Technology must be feeling like they are living in a dream, too.

In the quarter that stopped at the end of February (Q2 of Micron’s fiscal 2026 year), Micron’s revenues nearly tripled year on year to $23.86 billion, with operating income up by a factor of 9.1X to $16.14 billion and with net income up by 8.7X to $13.79 billion.

To put this into perspective, this second quarter of F2026 drove nearly as much revenue as all of fiscal 2024 – and with 17.7X more profits. There is no reason to believe that Micron and its memory and flash peers are not going to be coining money for the next year or two in ways we did not imagine were possible during the memory and flash bust only a few years ago.

Micron ended the quarter with $14.59 billion in cash – enough to build three-quarters of a memory fab – and spent a smidgen more than $5 billion on capital expenses to expand fab capacity that will come online two years or so from now.

The DRAM market is expanding on so many fronts, and Micron plays in all of them including datacenter-class LPDDR5 memory for AI servers like Nvidia’s “Grace” and “Vera” Arm server CPUs, DDR5 memory for high-end servers (generally X86 machines but also including IBM Power and z CPUs and various Arm designs), as well as the honey pot that HBM stacked memory has become. And consequently, Micron’s DRAM memory exploded in Q2 F2026, up 206.5 percent to an incredible $18.77 billion.

Here’s the fun bit. DRAM revenue increased 73.6 percent sequentially from Q1 F2026, but Mark Murphy, Micron’s chief financial officer, said that capacity shipped, measured in bits, only increased “mid-single digits” while the rest of that “in the mid-60s percentage range” was driven by bit price increases.

In any other time, the fact that the flash business grew by 1.7X year on year to just a tad under $5 billion would have been astonishing, but give the exploding DRAM market being up by more than 3X, the flash business pales by comparison. No one knows for sure how much of the flash business is for datacenter products, but I can confidently make this prediction: Going forward, there will be more concentration of flash capacity in the datacenter among all of the suppliers, which means our PCs, tablets, and smartphones are also going to get more expensive as too much demand chases too little supply. The same thing will happen to DRAM, of course.

Micron used to give a hint here and there so Wall Street could reckon how much of its DRAM business was coming through HBM stacked memory and how much was coming from high capacity server DIMMs and LPDDR5 low-powered server memory. Micron is Nvidia’s sole supplier for this, and it has just began shipping an LPDDR5 SOCAMM2 module that will allow Nvidia to quadruple the main memory capacity on the Vera CPU to 2 TB compared to a peak 512 GB for the Grace CPU. (For yield reasons, the capacity on Grace was actually lower than this, at 480 GB.)

Despite the dearth of hard data, I have continued to model out HBM memory revenues as well as for these other categories. My best guess is that HBM stacked memory revenues were up 7.3X year on year to $8.32 billion as Micron ships HBM3E memory for Nvidia “Blackwell” B300 GPUs and for AMD’s “Antares” MI325X, MI350X, and MI355X GPUs. Despite some rumors to the contrary, Micron is making HBM4 memory for the Nvidia “Rubin” R200 GPUs coming later this year; it is not clear if Micron has any slice of the AMD “Altair” MI400 series; the word on the street is that Samsung and SK Hynix will be the main suppliers of HBM for the initial members of this family of AMD accelerators.

My model says that high capacity server DIMMs plus LPDDR5 modules accounted for $1.46 billion in sales in Q2 F2026, up 39.5 percent. If you take this high-end and low-cost server memory out of the mix along with HBM, then the remaining DRAM business accounted for $8.99 billion in my model, up 128.2 percent. Still not bad, and illustrative of the across-the-board memory boom we are in.

Micron’s datacenter business is humming along quite nicely, as you can see.

The new Micron business units that comprise the datacenter business are the Cloud Memory and Core Datacenter units, and together, these units had $13.44 billion in sales, up 181.3 percent year on year, with an operating income of $8.94 billion, up 362.8 percent year on year.

Only a year and a half ago, this datacenter business was a relatively puny $3.5 billion with 29 percent of revenue dropping to the middle line. Now, it is 3.8X bigger and 8.7X more profitable. Somebody is going to get a whole lotta bonuses and stock options at Micron.

The shortages of DRAM and flash are helping the other Micron business units, as you can see in the table above. When the mobile and PC business is more profitable than the datacenter business and the auto and embedded business is almost as profitable as the datacenter business, you know something weird is happening.

It may sound like Micron is sitting in the catbird seat, but all suppliers have to be careful not to overextend their hand. Which is why Micron has just inked its first five-year strategic customer agreement, which is a whole lot different from the one-year long-term agreements it is used to signing.

It is natural to jump to the conclusion that this SCA was inked with Nvidia. But it could be Broadcom, which needs to secure HBM capacity for the homegrown accelerators being crafted by Google, Anthropic, Meta Platforms, ByteDance, and Apple. It could be Marvell, which will buy a much smaller volume of HBM memory compared to Nvidia and probably AMD, too, through its chip shepherding work for Microsoft and Amazon Web Services.

It is hard to say. Micron no doubt does not want to do a lot of such SCA deals because it limits its pricing levers. But it probably is a good thing to ink a few such deals so it can confidently build out a capacity expansion plan and fund it. All we know is the five year SCA comes at a time when Micron has committed to spend more than $25 billion on capex in fiscal 2026 and is looking out into the next several fiscal years at probably what used to seem like an enormous amount of capex investments that are now made possible by the GenAI boom.

Here’s the other thing: If Micron can make more DRAM and flash quicker than its rivals, it can steal market share in a boom time. You can bet Sanjay Mehrotra, the company’s chief executive officer, is thinking a lot about that – and how to not overdo it and create a bust cycle.