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What Su means by the “next phase” of AI
Su’s comments appeared as written responses in CRN’s 2026 CEO Outlook project, rather than as a conventional live interview. Her argument is that growing compute demand will require investment across the technologies that make AI systems work, not just in accelerators. Read CRN’s report of Su’s responses.
That points to a shift from AI experimentation and model training toward routine inference in production; from buying individual chips toward deploying integrated racks; and from cloud data centers alone toward PCs, enterprise systems, edge devices, robotics and industrial automation. AMD calls some of those real-world applications “physical AI.” Its strategy is to supply the compute components and software, then work with customers and partners to assemble them into systems for particular workloads.
This is a strategic thesis, not proof that every part of the portfolio is equally mature or that announced deployments have reached broad availability. The distinction between plans, customer commitments and delivered systems matters throughout the roadmap.
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Helios makes the system-level strategy concrete
Helios is AMD’s rack-scale AI platform, bringing together Instinct accelerators, EPYC server CPUs and Pensando networking, with ROCm software and open-standard technologies in the wider system. AMD positions it for large-scale inference, frontier-model training and fine-tuning. AMD’s Helios page describes the platform.
Why sell a rack, not just a GPU?
Large AI deployments depend on the movement of data among processors, memory and storage, as well as on networking, power delivery, cooling and serviceability. Coordinating those pieces can make it easier to deploy a system at scale and tune it as a whole. The trade-off is that an integrated rack can be more complex to procure and operate than a modular component purchase, and it may give a buyer less freedom to mix and match parts.
AMD’s Helios page specifies a 72-GPU rack design using MI455X accelerators, with 31 TB of HBM4 memory, 2.9 exaflops of FP4 compute and 1.4 exaflops of FP8 compute. These are AMD-published specifications, not independent benchmark results; they do not by themselves establish end-to-end performance, deployment cost or performance per watt. See AMD’s stated Helios specifications.
AMD’s event materials identify MI455X GPUs, “Venice” EPYC CPUs and Vulcano networking as components of the Helios architecture. These names describe the announced design; they should not be read as evidence that every component is already broadly available. AMD’s Advancing AI materials outline the platform.
EPYC is part of the AI platform, not an afterthought
Accelerators perform much of the specialized computation, but AI servers also need CPUs to coordinate work, prepare and move data, run control-plane functions, support virtualization and handle general-purpose tasks. EPYC therefore matters both in conventional server workloads and in the design of AI systems that pair CPUs with GPUs.
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AMD’s announced Meta deployment illustrates the pairing: it is scheduled to use custom MI450-based GPUs, sixth-generation EPYC “Venice” CPUs, Helios architecture and ROCm. AMD says Meta is a lead customer for Venice and the future workload-optimized “Verano” EPYC processor. Those are customer and product-roadmap statements, not independent performance measurements. AMD’s announcement describes the Meta agreement.
AMD positions the EPYC 9006 series for “agentic” data centers. That is a product-positioning claim; buyers should assess performance for their own applications, server configuration and software. See AMD’s EPYC 9006 series page.
Instinct: accelerator roadmap, rack deployments and customer-specific designs
Instinct is AMD’s accelerator family and its direct play in the AI GPU market. It is important to separate the chip roadmap from the systems and services built around it:
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- Integrated infrastructure: Helios combines accelerators with CPUs, networking and other rack-level elements.
- Custom deployments: AMD’s Meta agreement includes a customer-specific MI450-based GPU; that does not mean the customized design is identical to an off-the-shelf product.
- Cloud capacity: Cloud providers can offer access to systems without a customer buying and operating a full rack.
- Software and integration: ROCm and partner engineering help make hardware usable in particular environments.
AMD described the MI350 series as the fastest-ramping product in its history, a company characterization rather than an independently audited comparison. AMD has announced MI400-series products and previewed MI500-series GPUs. It said MI450 sampling had begun and that Helios production shipments remained on track for the second half of 2026 in its Q1 2026 earnings materials. Sampling and an “on track” schedule do not establish broad customer availability or completed deployments. See AMD’s AI strategy announcement and its Q1 2026 earnings-call transcript.
AMD has also expressed confidence in reaching tens of billions of dollars in annual data-center AI revenue in 2027. That is management’s outlook, not a guaranteed result. Delivering it depends on product execution, customer deployments, software adoption and the ability to manufacture and ship systems.
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ROCm is the adoption test
Accelerator hardware is only useful to customers if their models, frameworks and serving tools run effectively on it. ROCm is AMD’s software stack for programming and deploying workloads on AMD GPUs; AMD cites support for frameworks including PyTorch, TensorFlow and JAX. AMD’s ROCm page describes the software.
AMD says ROCm downloads increased tenfold year over year, an AMD-reported measure that does not, on its own, show how many production deployments use the stack or how much work those deployments run. In its Q1 2026 materials, AMD also cited expanded support for model families including Google Gemma 4, Qwen and Kimi, alongside increased investment in ROCm development. AMD reported the download figure and discussed model support in its earnings transcript.
Compatibility is not always plug-and-play. A model may run while still requiring changes to kernels, libraries, compilers or the serving stack. Teams whose tooling is built around Nvidia’s CUDA ecosystem should budget for migration and validation rather than assume that a framework-level compatibility statement means identical behavior or performance. AMD’s investment can reduce friction and broaden choice; the available evidence does not establish that ROCm has eliminated the software-maturity gap.
Ryzen AI takes the strategy to PCs and edge devices
Ryzen AI processors bring CPU, GPU and NPU resources to PCs. An NPU can run supported AI tasks locally, which may improve responsiveness and keep some data on the device instead of sending every request to a cloud service. Whether that is useful depends on the application, model, memory, software support and device’s thermal and performance limits.
In 2026 AMD introduced Ryzen AI 400 and Ryzen AI Pro 400 desktop processors. AMD said its AI PC portfolio had expanded 2.5 times since 2024 and that Ryzen powered more than 250 notebook and desktop platforms. These are company-reported portfolio figures; they do not establish how many systems have been sold or which AI applications run on each configuration. AMD’s Q1 2026 transcript covers the processor introductions and its stated platform count.
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AMD points to Dell, HP and Lenovo among the commercial PC partners supporting adoption. For industrial and robotics applications, AMD’s strategy also reaches adaptive and embedded products. These devices can bring compute closer to sensors or machines, but an AI PC NPU is not a substitute for a data-center accelerator used to train large models or serve high-volume inference.
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Partners turn announcements into systems—if deployment follows
AMD’s partner strategy spans several roles, rather than a single group of resellers:
- Hyperscalers and infrastructure builders: Meta is the clearest large deployment commitment in the cited announcements. AMD has also described expanded cooperation with Microsoft around Helios, EPYC Venice and Azure infrastructure. AMD’s Microsoft announcement sets out that cooperation.
- AI customers and model developers: AMD announced up to 2 gigawatts of MI450-series GPU deployment with Anthropic. The “up to” qualification matters: it is an announced deployment scale, not a statement that all capacity has shipped. AMD’s Anthropic announcement describes the agreement.
- OEMs and ODMs: Companies such as Dell, HPE, Lenovo, Supermicro, Sanmina, Wiwynn, Wistron and Inventec can design, build or integrate systems. Their role is important to turning platform designs into deployable equipment, but a named partner is not proof of broad production adoption.
- Software and infrastructure partners: ROCm ecosystem participants and companies such as Nutanix can help with software environments and deployment pathways.
- Manufacturing and packaging partners: ASE, SPIL and PTI are among the Taiwan ecosystem companies AMD named in its capacity-related announcement.
The Meta agreement covers up to 6 gigawatts of AMD GPUs across multiple generations. AMD scheduled shipments for the first gigawatt to begin in the second half of 2026. A gigawatt expresses power capacity or deployment scale, not a GPU-unit count or a revenue figure, and the “up to” agreement is contingent on execution and milestones. The deployment is to include a custom MI450-based GPU, Venice CPUs, Helios architecture and ROCm. AMD’s announcement provides the agreement’s terms and planned timing.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why manufacturing and packaging are strategic investments
AMD announced more than $10 billion in investments across Taiwan’s ecosystem to expand advanced packaging and strategic partnerships for AI infrastructure. The figure refers to investment across the ecosystem, not necessarily AMD capital expenditure alone. The announcement references EFB-based 2.5D packaging, work with ASE and SPIL, PTI panel-based interconnect efforts, and ODM partners intended to help move Helios toward high-volume manufacturing. AMD’s Taiwan announcement details the initiative.
Chip design is only one constraint on AI supply. Advanced packaging, integration with high-bandwidth memory, substrates, testing and rack assembly can all affect how quickly systems reach customers. Investment and partnerships may help expand capacity, but do not guarantee that supply will match demand or that every announced system will ship on schedule.
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What could limit the strategy—and what buyers should test
Su has pointed to rapid technology change, rising compute needs, power and efficiency constraints, integration complexity, operational risk and cost control as customer challenges. For a buyer, those concerns translate into practical checks before committing to a platform:
- Workload fit: Distinguish training, inference, fine-tuning, recommendation, agentic and high-performance-computing needs; they can demand different memory and networking configurations.
- Software portability: Inventory CUDA-specific kernels, libraries and tooling, then test the actual models, quantization methods and serving stack on the proposed AMD configuration.
- Facility readiness: Confirm power, cooling, networking and service capacity for the system’s real configuration, not just its headline compute specification.
- Availability: Treat “expected,” “scheduled” and “on track” as roadmap language until a vendor or integrator confirms a delivery window for the exact system.
- Support ownership: Establish who handles hardware, firmware, ROCm, model integration, monitoring and lifecycle issues across AMD, the OEM, cloud provider and software vendor.
- Economics and evidence: Compare total system costs—including electricity, cooling, integration, staffing and maintenance—and use independent benchmarks where available. Peak compute figures alone do not establish performance per dollar or per watt.
- Upgrade path: Ask whether future CPU, GPU and networking generations can be integrated without a major redesign, and what service and lifecycle commitments apply.
The trade-offs are real: open standards and a second supplier can offer flexibility, while an ecosystem with less established software support may require more engineering; a complete rack can simplify integration but reduce component choice; and a custom design can suit a hyperscaler’s workload without being a standardized product available to other buyers. Power, cooling, packaging supply and operating expertise can constrain deployment even when silicon is ready.
What to watch as 2026 unfolds
AMD’s thesis will be tested by delivery and sustained use, not announcements alone. The most informative milestones are whether Helios production shipments and MI450 availability meet their stated schedules; how Venice appears in customer systems; whether ROCm supports the specific models and tooling buyers need; and whether announced hyperscaler commitments become operating deployments. Independent benchmark results, packaging and memory supply, and the adoption of Ryzen AI systems in commercial fleets will help show how far the strategy extends beyond roadmap claims.
For investors, AMD’s stated ambition for tens of billions of dollars in annual data-center AI revenue in 2027 is an outlook to track against reported results, not an assurance. For enterprise buyers, the nearer-term question is more concrete: can AMD and its partners deliver a supported system that fits the workload, software environment, facility and deployment timetable?
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