At the AMD Advancing AI event in San Jose earlier this month, CEO Lisa Su and her staff showcased the company’s progress across many different facets of AI. They had plenty to announce in both hardware and software, including significant performance gains for GPUs, ongoing advances in the ROCm development platform and the forthcoming introduction of rack-scale infrastructure. There were also many references to trust and strong relationships with customers and partners, which I liked, and a lot of emphasis on open hardware and an open development ecosystem, which I think is less of a clear winner for AMD, as I’ll explain later.

Overall, I think the event was important for showing how AMD is moving the ball down the field for customers and developers. Under Su, AMD’s M.O. is to have clear, ambitious plans and execute against them. Her “say/do” ratio is high. The company does what it says it will do. This is exactly what it must continue doing to whittle away at Nvidia’s dominance in the datacenter AI GPU market. What I saw at the Advancing AI event raised my confidence from last year — although there are a few gaps that need to be addressed.

(Note: AMD is an advisory client of my firm, Moor Insights & Strategy.)

When she took the stage, Su established the context for AMD’s announcements by describing the staggering growth that is the backdrop for today’s AI chip market. Just take a look at the chart below.

So this segment of the chip industry is looking at a TAM of half a trillion dollars by 2028, with the whole AI accelerator market increasing at a 60% CAGR. The AI inference sub-segment — where AMD competes on better footing with Nvidia — is enjoying an 80% CAGR. People thought that the market numbers AMD cited last year were too high, but not so. This is the world we’re living in. For the record, I never doubted the TAM numbers last year.

AMD is carving out a bigger place in this world for itself. As Su pointed out, its Instinct GPUs are used by seven of the 10 largest AI companies, and they drive AI for Microsoft Office, Facebook, Zoom, Netflix, Uber, Salesforce and SAP. Its EPYC server CPUs continue to put up record market share (40% last quarter), and it has built out a full stack — partly through smart acquisitions — to support its AI ambitions. I would point in particular to the ZT Systems acquisition and the introduction of the Pensando DPU and the Pollara NIC.

GPUs are at the heart of datacenter AI, and AMD’s new MI350 series was in the spotlight at this event. Although these chips were slated to ship in Q3, Su said that production shipments had in fact started earlier in June, with partners on track to launch platforms and public cloud instances in Q3. There were cheers from the crowd when they heard that the MI350 delivers a 4x performance improvement over the prior generation. AMD says that its high-end MI355X GPU outperforms the Nvidia B200 to the tune of 1.6x memory, 2.2x compute throughput and 40% more tokens per dollar. (Testing by my company Signal65 showed that the MI355X running DeepSeek-R1 produced up to 1.5x higher throughput than the B200.)

To put it in a different perspective, a single MI355X can run a 520-billion-parameter model. And I wasn’t surprised when Su and others onstage looked ahead to even better performance — maybe 10x better — projected for the MI400 series and beyond. That puts us into the dreamland of an individual GPU running a trillion-parameter model.

By the way, AMD has not forgotten for one second that it is a CPU company. The EPYC Venice processor scheduled to hit the market in 2026 should be better at absolutely everything — 256 high-performance cores, 70% more compute performance than the current generation and so on. EPYC’s rapid gains in datacenter market share over the past few years are no accident, and at this point all the company needs to do for CPUs is hold steady on its current up-and-to-the-right trajectory. I am hopeful that Signal65 will get a crack at testing the claims the company made at the event.

This level of performance is needed in the era of agentic AI and a landscape of many competing and complementary AI models. Su predicts — and I agree — that there will be hundreds of thousands of specialized AI models in the coming years. This is specifically true for enterprises that will have smaller models focused on areas like CRM, ERP, SCM, HCM, legal, finance and so on. To support this, AMD talked at the event about its plan to sustain an annual cadence of Instinct accelerators, adding a new generation every year. Easy to say, hard to do — though, again, AMD has a high say/do ratio these days.

On the hardware side, the biggest announcement was the forthcoming Helios rack-scale GPU product that AMD plans to deliver in 2026. This is a big deal, and I want to emphasize how difficult it is to bring together high-performing CPUs (EPYC Venice), GPUs (MI400) and networking chips (next-gen Pensando Vulcano NICs) in a liquid-cooled rack. It’s also an excellent way to take on Nvidia, which makes a mint off of its own rack-scale offerings for AI. At the event, Su said she believes that Helios will be the new industry standard when it launches next year (and cited a string of specs and performance numbers to back that up). It’s good to see AMD provide a roadmap this far out, but it also had to after Nvidia did at the GTC event earlier this year.

On the software side, Vamsi Boppana, senior vice president of the Artificial Intelligence Group at AMD, started off by announcing the arrival of ROCm 7, the latest version of the company’s open source software platform for GPUs. Again, big improvements come with each generation — in this case, a 3.5x gain in inference performance compared to ROCm 6. Boppana stressed the very high cadence of updates for AMD software, with new features being released every two weeks. He also talked about the benefits of distributed inference, which allows the two steps of inference to be tasked to separate GPU pools, further speeding up the process. Finally, he announced — to a chorus of cheers — the AMD Developer Cloud, which makes AMD GPUs accessible from anywhere so developers can use them to test-drive their ideas.

Last year, Meta had kind things to say about ROCm, and I was impressed because Meta is the hardest “grader” next to Microsoft. This year, I heard companies talking about both training and inference, and again I’m impressed. (More on that below.) It was also great getting some time with Anush Elangovan, vice president for AI software at AMD, for a video I shot with him. Elangovan is very hardcore, which is exactly what AMD needs. Real grinders. Nightly code drops.

So that’s (most of) what was new at AMD Advancing AI. In the next three sections, I want to talk about the good, the needs-improvement and the yet-to-be-determined aspects of what I heard during the event.

Let’s start with the good things that jumped out at me.

While overall I thought Advancing AI was a win for AMD, there were two areas where I thought the company missed the mark — one by omission, one by commission.

In some areas, I suspect that AMD is doing okay or will be doing okay soon — but I’m just not sure. I can’t imagine that any of the following items has completely escaped AMD’s attention, but I would recommend that the company address them candidly so that customers know what to expect and can maintain high confidence in what AMD is delivering.

• As I’ve discussed before, AMD has made great strides with its processors (both GPUs and CPUs) with hyperscalers. In the enterprise datacenter? Less so. Progress with enterprise GPUs seems unclear, and I’d love to see an announcement of a nice customer win or two here.
• As just mentioned, hyperscalers have been very important for AMD’s success over the past few years, and any chip vendor would love to hear the glowing words that Microsoft, Meta and Oracle offered onstage at this event. But AWS and Google are MI-series holdouts so far. That said, if the CSPs plan to announce AMD adoption, they would likely prefer to do it on their own terms at their own events (e.g., AWS re:Invent toward the end of the year). Both AWS and Google have made significant investments in their own XPUs, so would they focus on AMD for inference? Ultimately, I think we will see them come on board, but I don’t know when.

It is very difficult to engineer cutting-edge semiconductors — let alone rack-scale systems and all the attendant software — on the steady cadence that AMD is maintaining. So kudos to Su and everyone else at the company who’s making that happen. But my confidence (and Wall Street’s) would rise if AMD provided more granularity about what it’s doing, starting with datacenter GPU forecasts. Clearly, AMD doesn’t need to compete with Nvidia on every single thing to be successful. But it would be well served to fill in some of the gaps in its story to better speak to the comprehensive ecosystem it’s creating.

Having spent plenty of time working inside companies on both the OEM and semiconductor sides, I do understand the difficulties AMD faces in providing that kind of clarity. The process of landing design wins can be lumpy, and a few of the non-AMD speakers at Advancing AI mentioned that the company is engaged in the “bake-offs” that are inevitable in that process. Meanwhile, we’re left to wonder what might be holding things back, other than AMD’s institutional conservatism — the healthy reticence of engineers not to make any claims until they’re sure of the win.

That said, with Nvidia’s B200s sold out for the next year, you’d think that AMD should be able to sell every wafer it makes, right? So are AMD’s yields not good enough yet? Or are hyperscalers having their own problems scaling and deploying? Is there some other gating item? I’d love to know.

Please don’t take any of my questions the wrong way, because AMD is doing some amazing things, and I walked away from the Advancing AI event impressed with the company’s progress. At the show, Su was forthright about describing the pace of this AI revolution we’re living in — “unlike anything we’ve seen in modern computing, anything we’ve seen in our careers, and frankly, anything we’ve seen in our lifetime.” I’ll keep looking for answers to my nagging questions, and I’m eager to see how the competition between AMD and Nvidia plays out over the next two years and beyond. Meanwhile, AMD moved down the field at its event, and I look forward to seeing where it is headed.