The Money AMD Is Chasing With Its Rackscale AI System Roadmaps
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The Money AMD Is Chasing With Its Rackscale AI System Roadmaps
Timothy Prickett Morgan
Timothy Prickett<br>Morgan
Co-Editor, Co-Founder, The Next Platform
Published<br>fri 24 Jul 2026 // 19:22 UTC
This week, AMD held what is now an annual Advancing AI event in Silicon Valley, and there is much talk about money, roadmaps, and product deep dives.<br>Like AMD chief executive officer Lisa Su, who has without a doubt not only saved the chip maker from some self-destructive tendencies but has broadened and deepened its lineup of hard and soft wares to make it an absolutely credible alternative to AI industry juggernaut Nvidia and former CPU rival Intel, we will start off our coverage of the event with an update on total addressable markets and the forces that drive them.
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In the coming days, we will uncover the details of the “Altair” MI400 architecture, specifically for the MI455X variant that makes its debut in AMD’s “Helios” rackscale server designs later this year through the many OEM and ODM partners that have all jumped on the AMD bandwagon. We will also take a look at new details about the forthcoming “Venice” Epyc 9006 processors also expected later this year. And then we will take a look at the roadmaps that AMD gave some sneak peeks of, which will help them capture AMD’s fair share of the opportunities.<br>So, without further ado, let’s talk money.<br>In her opening keynote address, Su said that AI training workloads are increasing their compute needs by a factor of 5X every year since 2020, and that there is no signs of this abating. That drives a certain amount of compute for the AI model builders, large and small. But the big driver, according to Su, is the inevitable and much-anticipated shift from AI training at a relative handful of companies to AI inference with companies either renting GenAI models through APIs or buying AI systems to run their own inference with licensed or open source models.<br>That latter shift has been long anticipated, and the projections from several years ago, before GenAI hit, showed that inference would eventually drive maybe 3X or 4X the compute (and therefore revenues) as training. We are not quite there yet, but we are apparently on our way, according to Su, who proclaimed at last year in the AI accelerator market, inference and training were split about 50-50, but this year she expects for it to look more like 60 percent for inference and 40 percent for training. It was about 40-60 back in 2024:
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The question we all want to know is when will it be 2:1, 3:1, or 4:1 for inference, or even 10:1. When that happens, that will mean that AI inference – and specially generative AI inference rather than traditional machine learning – really is a production workload for the enterprises as well as hyperscalers, cloud builders, and AI model builders. (By the way, if you tear apart some of the charts Su presented and reconstitute the data underlying those charts, you can derive a revenue ratio between AI training and inference, and even between GPU/XPU training and agentic on CPU plus<br>The curve for token consumption worldwide that Su showed off came from the State of the AI Economy report from Exponential View, and it shows the monthly increases in the number of tokens that are consumed or generated by GenAI models from January 2024 through February of this year:
Exponential View estimated that an astounding 35 quadrillion tokens per month were chewed up or spat out in February, and given this exponential curve it should be well into 50 quadrillion tokens per month here as we finish July in eight days. All of the AI model builders and the hyperscalers and cloud builders that are serving them as well as themselves in the GenAI boom are hoping like hell that this curve keeps rising, therefore justifying the hundreds of billions of dollars in AI system capacity they are building out this year. The only way the curve can keep growing is to have more machines, but there is always the inverse of the Field of Dreams as a possibility: You build it, and they don’t come.<br>So far, demand for AI processing is exceeding supply, and that is why pricing for all key components –CPUs, GPUs, DRAM memory, flash, switch ASICs, optical components – are all rising along a similar curve.
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Su’s job as the top executive at AMD is to be optimistic about the future in terms of such exponentials and about the affect that HPC in general and GenAI in particular will have on the world, and the total addressable markets that AMD is chasing and divulging at the Advancing AI event reflect this.<br>“When I think about where AI is today, the biggest change that we see is we are no longer talking about what might be possible,” Su said at the end if several hours of presentations by the top brass at AMD. “We are actually seeing how AI can have real and significant...