by DiabloD3 | 2026-03-05 09:29:27+00:00
They can't fix stupid.

Let me describe this in the most simple terms possible: You have speculators speculating about AI products. The speculators are not very smart when it comes to technology, and think RAM is RAM. There is at least three kinds of RAM that are important to this: DDR for system RAM, GDDR for GPUs, and HBM for high density enterprise products, and they are not interchangeable, there is no one-die-fits-all solution.

So, these speculators are like "oh no, more GPUs requires more RAM!", and then just start speculating on all RAM. Which of these RAMs are the ones that they need to worry about? Exclusively HBM, which is a minority in production, DDR and GDDR dominate production.

If you're into inference, and have older machines, you're buying Hxxx or Bxxx cards that use HBM, fit into dual slot x16 configurations, and you're jamming (optimally) 8 of them in. If you're into hardware that is newer, somewhere in the middle of the inference boom, you're using MXM cards. In either situation, the host machine has DDR, but if you're OpenAI, Anthropic, Microsoft, or Google, you're not building (more) inference machines like this.

The first two are buying Nvidia's all in one SBC solution: unified HBM, onboard ARM CPU to babysit the dual GPUs, has its own dual QSFP network controller that can RDMA, etc. No DDR or GDDR involved. Any machines built before this platform are being phased out entirely.

Microsoft is doing the same, but with AMD's products, the MI series that co-locates Epyc-grade Zen 4/5 CCDs with CDNA compute chiplets, running the entire thing off HBM, thus also unified and no DDR/GDDR needed. They, too, are phasing out machines older than this.

Google has a mix: they offer Nvidia all in one SBCs as part of GCP for legacy inference tasks (so your stack that can't run on AMD yet still can run), but also offer the same MI products that Microsoft offers via Azure's inference product, but also has their own TPUs that some of Gemini runs on; the TPUs run on HBM afiact. No DDR or GDDR here.

So, what does AMD or Intel do here? Lets say they waste fab time to make their own dies on the wrong process (TSMC and Intel-Foundry do not have for-RAM optimized processes)... they would be producing DDR and GDDR for a market that almost has its entire demand met. Intel lacks the die stacking technology required to build HBM, and TSMC I think can't do it for that many layers (HBM has 8 to 16 layers in current gen stuff iirc).

Micron, for example, already is bringing two large factories online here in the US to meet the projected growth in demand for the next 20+ years. When these factories finally start producing, it will not change the minds of speculators: they still seem to think AI datacenters need RAM, of any kind, and refuse to understand even the most basics of nuance. Also, when they come online, HBM will be a minority product; the AI inference boom is still just a bump in the road for them.

Nvidia kinda screwed their consumer partners, btw: they no longer bundle the GDDR required for the card with the purchase of the die. There is a slight short term bump in GDDR spot prices as partners are building up warchests to push series 60 GPUs into production, and once that is done, spot prices return to normal (outside of the wild speculation manipulation).

One last thing: what about LPDDR, used by AMD Strix Halo and Apple stuff? Speculation seems to have not actually effected it. I consider it as a sub-category of DDR (and some dies seem to work as either DDR or LPDDR as of DDR5, due to the merger of the specs by JEDEC), but since it isn't something you find in datacenters, it seems to have avoided speculation.

The Ryzen Max CPUs mentioned in the linked article? Uses LPDDR. Doubling down on the Ryzen Max product line might be a brilliant move.