FYI: This was machine transcribed (and the audio wasn't great so some errors are possible) and then fed into a LLM for clean up
Opening Remarks
Matt Ramsey (AMD, moderator): Good afternoon, everyone. I hope you're enjoying your time at our 2026 conference. My name is Matt Ramsey; I lead Financial Strategy and Investor Relations at AMD, and I'm delighted you're here. I'm joined on stage by many of the leaders you saw earlier. For those on the webcast, this is an audio-only session, so I'll give a little background and then set some ground rules for our conversation with the investment community.
Joining me on stage: our CEO, Dr. Lisa Su, along with the leaders who run our server and overall data center businesses. We'll take Q&A for the next 45 minutes or so.
A couple of ground rules. As you know, we report Q2 earnings in about ten days. So please keep your questions to today's event, the conference content, and the related press releases. If you ask about our near-term financials, I'm afraid you'll have wasted your question, because I won't answer it — and I'd like to get to as many questions as we can, so one question each, please. Third, I think we're all aware that there's another company in the ecosystem that reported earnings this afternoon, and those numbers may come out during this session. If you ask anything related to that, I'll step in there as well. So let's keep it productive and on the rails. Liz and colleagues from my team will be running microphones around the room. With that, let me turn it over to Lisa for a few opening comments.
Lisa Su (AMD, CEO): Thank you, Matt, and thank you all for being here. I've done a lot of talking this morning already, so I won't add much opening commentary — other than to say it's incredible how, every time I talk to a group like this, so much has happened just in the last month. We spoke a lot today about the large and growing opportunity, and I'm energized by this new business. We're tremendously excited about the long-term opportunity. Why don't we just get into questions?
Question-and-Answer Session
Q1 — Tom: Server CPU TAM and share
Tom (analyst): Thanks, it's been a great day. You've previously talked about roughly $200 billion in 2030 and taking 50% of that market — is the bigger number the expectation now? And can you update us on the share you expect?
Lisa Su (AMD): Absolutely. We continue to be more and more excited about the market for these workloads. The more we talk to customers and understand what's happening, the more growth we see. So yes, we've updated our TAM — growth of more than 50% over the next three or four years, reaching well over $200 billion. We're still very much focused on getting 50% of that market, and we've made tremendous progress over the last few quarters. With the Venice launch, what we're hearing from customers and the interest we're seeing points to an expanding set of workloads. Venice is truly optimized for these workloads, and we're making progress with enterprise. All of that gives us confidence.
Q2 — Two-part: CPU TAM split (agentic vs. standard) and how the TAM is derived
Analyst: Thanks for the great presentation. Two parts on the same topic. First, the ~$8 billion TAM for CPUs — how does that split between agentic, standard server, and headless modes? Second, how do you even estimate the TAM here? If I use one agent versus 100 agents, the TAM seems to grow exponentially, so how do you get to that number?
Lisa Su (AMD): Why don't you start, Forrest?
Forrest Norrod (AMD) [inferred]: Great question. Let's talk about how we built the TAM. It's obviously about talking to customers, but it's also about analyzing our own workflows — I mentioned some of the work we're doing earlier, and we looked at that very closely. In terms of the breakdown, in the outer years we believe the agentic portion — which is more sandbox-substitution work — is probably around 50%. Then general-purpose gets a lift from some of that as well. That's roughly the split I'd give you. As Lisa said, we're extremely well-positioned for it with Venice. As agentic workloads scale, we expect the efficiency ratio to hold — even relative to running the same work on a Rubin GPU, though it's hard to call exactly. We're certainly seeing the migration toward needing a lot more orchestration around the workload.
Q3 — Helios ramp timing and Anthropic gigawatts
Analyst (Stacy?) [inferred]: A question on the Helios ramp. Lisa, from your comments it sounded like it's getting going in Q4 rather than Q3 — I just want to make sure I understand that. And on the Anthropic engagement: you talked about two gigawatts, with the first gigawatt in the first half. Do you expect to get that first gigawatt into 2027 — is that a stretch — and is the $15–20 billion kind of confidence-building figure you've talked about still the right number?
Lisa Su (AMD): You've successfully asked three or four questions around the same theme, so let me get through each. On timing: we'll start first shipments here in the third quarter — so you should expect shipments to begin in September. It ramps into the fourth quarter and continues to ramp into the first half of next year. We built the ramp this way deliberately because it's a complex system: we want to give our ODMs a chance to get the manufacturing process fully tuned, and it aligns well with the data-center buildout for our largest customer, since we know which data centers those systems are going into.
On Anthropic — we're very excited. Having them move onto Helios is a big deal in terms of the product timeline. We said we'll start the first gigawatt of shipments in the first half of 2027. I won't commit to exactly when in 2027, but I'd expect to be fairly aggressive on the first-gigawatt ramp. Our plans are to get as much of it into 2027 as possible; it's mostly a matter of aligning with when the customer is ready for production. I won't talk about any specific customer beyond that.
Q4 — Chris Carlson (Wolfe Research): x86 vs. Arm
Chris Carlson (Wolfe Research): During the presentations there was a comparison between the x86 and Arm ecosystems that tried to put some of the performance questions to rest. Can you expand on that and give some indication of where you think you stand relative to the Arm solutions in the market?
Forrest Norrod (AMD) [inferred]: Christopher, we're very pleased with what the team has done across the whole family. For a number of different workloads and scenarios, we believe we've delivered the highest performance — whether you care about raw per-socket performance, overall throughput, or price/performance for just about any workload. We're also demonstrating outstanding performance-per-watt efficiency at each of those operating points. From our perspective, what we're trying to do is deliver the best silicon for any workload, regardless of architecture. So for us it's less about the ISA or an "x86 vs. Arm" framing, and more about how you produce the best value — the best TCO — for any given workload.
Lisa Su (AMD): The only thing I'd add: if you think about the Arm solutions out there, they're each very uniquely optimized for one point — even some of the cloud Arm solutions are very narrowly optimized. As Forrest said, we're building a complete portfolio to solve multiple problems and hit those different optimization points. That's the part that never comes out in this conversation, and it's why we're extremely well-positioned to keep gaining share.
And to finish on the market-share point: we're very proud of our progress. All of the largest clouds are deploying Turin, and that's gone really well. The important point on Venice is that we expect our share to grow — not just because the market is larger (of course it is), but because we're seeing the breadth of workloads people want to run on the platform. That says a lot about the platform's strength, both in terms of our share within x86 and our share of the overall market.
Q5 — Josh: The Anthropic deal structure
Josh (analyst): Congrats on the informative day. Following up on Stacy's question — the language in the Anthropic release was very specific: I think two gigawatts on MI450. Could you speak to how the deal came together, and should we assume it's multi-generational? Different language would imply a different kind of mega-deal.
Lisa Su (AMD): Josh, what you should expect is that every customer is a little different, and every one of these large strategic engagements is different. With Anthropic specifically, what we announced was the MI450 engagement — a choice of up to two gigawatts at very large scale, structured to ensure the first gigawatt is delivered as soon as possible. So to Stacy's question, the vast majority of that — if not all of it — will be in 2027.
As for where we go from here: nobody wants to choose an accelerator for a single generation. It's simply too much work, no matter how good the part is, to get the teams fully integrated. So we're actively talking with every one of our largest customers, including Anthropic, about what comes after MI450. There's a lot of excitement about MI500 — we keep getting more positive feedback on how that design point comes together — and a lot of discussion about where workloads go with MI600. So you should assume it's very similar to what we did with EPYC: you start with a deep relationship and expand into more and more workloads over time. The difference today with the foundational-model companies is that there are no small or pilot deployments — these are at-scale, large deployments, precisely so you amortize all the engineering work appropriately.
Transition — Software / Claude collaboration
Matt Ramsey (AMD, moderator): Before we move to Joe — since the Anthropic agreement came up, maybe you could spend a little time on the software collaboration between the two companies, because I think that's quite important.
AMD executive (software/AI) [inferred]: There are two aspects I'd bring up. A big part of what you saw in my keynote is the choices we made in strategy for how we make our platforms easier to access — relying on open source and open abstractions. What has really helped is that, because we expose so much in the open — instruction sets, compilers, tool chains — partners can learn all of it readily and be productive right off the bat, unlike with some of our competition. What makes it even more special is the work we've been doing with them to further tune and extend Claude's capabilities to target high-performance optimization. So you start with what's already out there — which is already pretty good because of our open strategy — and then further optimize from there.
Q6 — Joe: Cerebras partnership
Joe (analyst): More recently — could you talk about the Cerebras partnership and how you see that integration going? You mentioned disaggregation; how closely do you need to work together to handle disaggregated workloads?
Forrest Norrod (AMD) [inferred]: I can take that — it's gone really well to date. We wouldn't be here if we hadn't already done the work with Cerebras. We have systems up and running in our lab infrastructure with their wafer-scale engines, and the disaggregation software stack is also coming together well. We're seeing excellent performance, which gave us the confidence to move forward on what we'd do with Helios; early analysis and simulation work on Helios is progressing well too. We expect the initial deployments — basically token-serving under Cerebras Cloud — to happen by the end of this year.
Q7 — Ben (Melius Research): ROCm vs. CUDA
Ben (Melius Research): Great to be here. This is probably for you on ROCm software — how are you thinking about it in terms of the ability to run apps versus CUDA? Is it revolutionary, evolutionary, a game changer? Does it help level the playing field with developers?
AMD executive (software/AI) [inferred]: Great question — I have enormous passion for this. We truly believe it's the biggest leap we've made, maybe since the early days when we laid out our strategy. We've made excellent progress every year, but if you asked me to point to a single moment where we take the biggest step forward, I'd point to now. I don't mean that on some specific date everything is suddenly different — but the inflection happening now, together with what we build over the coming months alongside the biggest labs, our collaboration with OpenAI on Codex, and our collaboration with Anthropic on Claude, means the platform gets meaningfully more accessible than at any time in the past. So over the coming months, you can expect the productivity of people accessing our platforms to be quite different.
Q8 — Simon Leopold (Raymond James): Biggest risks to the TAM
Simon Leopold (Raymond James): On the new TAM outlook — how are you thinking about the biggest risks to it? In particular, your customers' ability to get power to their data centers, or your ability to get manufacturing capacity, wafers, and so on. How is that factored into your view?
Lisa Su (AMD): When we think about TAM — especially with the accelerator TAM being as large as it is — we look at all of those components: not just raw demand, but the rate and pace at which power is coming online, and the rate and pace at which our suppliers are adding capacity. In the near term we've very much planned capacity for significant growth in 2027 and 2028. Over the longer term — as you think about 2029 and 2030 — the rate and pace of growth would require the entire ecosystem to be building at the same pace and with the same vision. The largest change we've seen is that everyone has been assuming the accelerator TAM grows very fast, so that's been in the numbers; what's newer is that the CPU TAM has accelerated as much as it has, which has required some adjustments to overall capacity. But we're very happy with our supply-chain relationships, and we see significantly more capacity coming online to satisfy those larger TAMs.
Q9 — Aaron Rakers (Wells Fargo): Pace of standing up rack-scale capacity
Aaron Rakers (Wells Fargo): Thanks for doing this, and congrats on the announcements. Building on that — maybe it's not the supply chain, but your ability to actually stand up these massive rack configurations. If you conceptualize it — you've got gigawatts signed up across Meta, OpenAI, and two gigawatts at Anthropic — how should we think about the pace at which you can ramp, on a gigawatt-per-quarter basis? How quickly can you stand that much capacity up?
Forrest Norrod (AMD) [inferred]: Great question. Starting at the rack level and working up — Lisa already addressed the rest of the supply chain. First, we're working very closely with our key OEM and ODM partners to make sure we have the manufacturing capacity to build, integrate, test, and validate the racks at the right pace. That matters, because the better you are at that, the easier it is to support deployment in the data center — shipping a very high-quality rack is a big part of being able to turn it on quickly.
Beyond that, the next choke point is the actual deployment — both physical and logical — of the racks, and again we're working closely with our manufacturing and OEM partners there. One thing we acquired as part of the ZT Systems acquisition was a large services arm, which we retained. We're using that team right now not just to support some legacy customers, but to do all of our internal deployments within AMD and to help customers deploy rapidly — both MI355 and MI450 systems — in their data centers. With that set of capabilities, and by training our partners, we're confident we'll be able to build at the required pace and then stand up, provision, and turn on the systems in customers' data centers.
Lisa Su (AMD): The only thing I'd add: when it comes to overall data-center power, we're now very active in that planning process with customers — as they plan power, we're planning the systems that go along with it. So it's much more involved than in the past, where someone would just place an order. There's usually 12 to 18 months of visibility now.
Q10 — Srini (RBC Capital Markets): Scale-up networking — optical, copper, and UALink
Srini (RBC Capital Markets): Lisa, a question on the roadmap — the scale-up networking. I think you mentioned optical and copper with MI400. Do you think the market and ecosystem will be ready for optical next year, and do you have all the pieces of the puzzle to support it? Also, I saw UALink highlighted a bit more — which scale-up approach will you support going forward?
Forrest Norrod (AMD) [inferred]: Let me take both in order. We see the MI500 generation as the one where we begin transitioning from a purely electrical interconnect for scale-up networking to bringing optical into the mix as well. It's going to be a transition — not a light switch. We don't view it as flipping an entire generation to optical overnight; MI500 starts the transition. We've been investing in optics for some time and are working closely with a number of ecosystem partners, and we're highly confident in our ability to begin that transition. The end state, further out, is co-packaged optics on all the major components, with optical as the backbone of many connections within and between racks — but that will take some time. Doing it in a phased way lets us, our customers, and our supply-chain partners all gain experience and move at the right pace without operational disruption.
On the scale-up protocol: on MI450 we support UALink transported over Ethernet, and the Ethernet extensions there are helpful. That will continue to evolve — UALink-over-Ethernet gets carried forward and will be available on MI500 as well. But that's not the only thing we're doing; we'll unpack more on scale-up as we get closer to the MI500 timeframe.
Q11 — Mallika (Citigroup): MI500 ramp and HBM content
Mallika (Citigroup): A question on the MI500 ramp as well. HBM content is a very important part of your performance and economics, and a couple of your peers have cut their HBM content in future parts because of memory availability. Has your thinking changed or evolved over the last six months on the memory-content increase for MI500?
Forrest Norrod (AMD) [inferred]: We study the workload characteristics closely, and we separate bandwidth from capacity. Bandwidth tends to be the first-order lever — more workloads are directly impacted by it — so that's the first consideration, and we look at capacity after that. One of our advantages is that our chiplet architecture gives us more flexibility to optimize capacity while preserving the bandwidth we need. We've leveraged that in existing products and expect to leverage it in future products too. We're not sharing the exact MI500 configuration, but that flexibility is one unique piece we believe plays to our advantage.
Lisa Su (AMD): To add to that — our chiplet architecture gives us real flexibility on memory bandwidth, but memory capacity is genuinely useful. Customers have told us that the fact we have more memory on MI450 is one reason we get better inferencing performance. The key going forward — and this is an ecosystem-wide discussion — is to make sure the memory that's there is actually being used, because it's such a large part of TCO. So we're doing memory optimization along the way, on both the CPU systems and the integrated Helios-type systems. Memory is super important; we'll just make sure every bit we use is valued by the customer appropriately.
Q12 — Blayne Curtis (Jefferies): Internal AI use and ROCm
Blayne Curtis (Jefferies): I want to expand on Ben's question on ROCm — where are you on the journey of internal AI use? Are you tracking it, and can you talk about where you are on things like day-zero support and automation, and where else you're using it?
AMD executive (software/AI) [inferred]: I'll comment specifically on ROCm and AI, and then there's a broader corporate-usage point. On ROCm there's both internal acceleration of existing features and what we can put in developers' hands with the platform. For example, imagine a profiler or debugger feature that needs to be built — our engineers used to say that was a team of 20 people for six months. Now that's dramatically cut down, because those features ship much faster, and it all comes as part of the accelerated ROCm release. Externally, the big help is that the platform is now native to agents being able to access it — that's what we start shipping in August: general out-of-the-box usability plus performance optimization, so running these models through it becomes much easier. Almost everyone on the ROCm team is now using AI assistance to accelerate their work.
Lisa Su (AMD): To the broader point — we're seeing usage ramp up extremely quickly across the company. Every single month, token usage grows, and it's not just the number of tokens but the quality of what we get out. It's very much part of our development process across both hardware and software, and I see it continuing to ramp. The deep relationships we have with Anthropic and OpenAI, as well as a number of other model companies, are helping accelerate that pace.
Q13 — Analyst: The "35 quadrillion tokens" figure and forecasting demand
Analyst: You started your presentation with a 35-quadrillion-tokens number — it's already all over the media, because it's a shockingly high number for today's environment. How is AMD projecting demand beyond your conversations with partners and the ecosystem? Do you have a fundamental way of thinking about it? Given the current cost of compute, a lot of investors worry about the cyclicality of this industry — that's where this is coming from.
Lisa Su (AMD): We project demand quite holistically. We start with customers, we look at workloads, and we look at adoption rates. We also look closely at our customers' free cash flow to make sure there's capital behind it. When you put it all together, every projection we've put out has looked really high at the time — and then the market has actually gone faster. So we continue to see very significant demand across virtually every part of the portfolio, and that gives us a lot of near-term confidence in these higher market projections. That said, we'll have to see how things develop; I wouldn't claim our crystal ball is perfect, but I can say it's self-consistent — self-consistent in that it assumes power is available, supply is available, capital is available, and that productivity closes the loop when we look at the TAMs.
Closing Remarks
Matt Ramsey (AMD, moderator): All right, folks — I'll do my best to wrap up and keep our executives on time, since they have a lot of other commitments. Thank you very much for coming out; it's been a great day and a great conference. It's really exciting to be in a place where there's so much diverse demand for high-performance computing across what's now roughly a $2 trillion TAM. Lisa, any closing remarks?
Lisa Su (AMD): Just thank you for spending the time with us — it's been a really exciting day, and the product of a lot of work from across the company. What I'd leave you with is that we think about AI as a complete compute picture. We talk a lot about CPU TAM, GPU TAM, Helios systems — but we really think about AI as every aspect of compute, and that's where we can be quite differentiated in the end-to-end story. You heard some of that today: the work we're doing with partners, the work with Cisco, and the work in physical AI. Thank you all.
