Advanced Micro Devices (NASDAQ:AMD) executives said the company is expanding its role in artificial intelligence from a semiconductor supplier toward a provider of rack-scale systems and software, while positioning its server CPU portfolio for increased demand from agentic AI workloads.
Speaking at the Goldman Sachs Communacopia + Technology Conference, Dan McNamara, AMD’s senior vice president and general manager of Compute and Enterprise AI, said AMD has also become a significant internal user of AI. The company began by optimizing its data layer for AI agents and open-sourced a solution called Optima, he said.
“When you can drive a faster time to market, that’s where the real rubber hits the road,” McNamara said, adding that AMD deploys its own technologies in its data centers before bringing them to external enterprise customers.
Internal AI Adoption and Model Mix
Matt Ramsay, AMD’s corporate vice president of financial strategy and investor relations, said the company has adopted tools from Anthropic and OpenAI across its engineering organizations to accelerate software development, debugging and chip-program production. He said AMD has incurred higher token costs but has seen larger productivity gains.
On the outlook for AI models, Ramsay said the market will likely include frontier models, open-weight models and smaller language models tailored to specific tasks. AMD’s objective, he said, is to provide CPU and GPU products that deliver favorable token-per-dollar economics across those use cases.
McNamara said enterprises are expected to use hybrid architectures, combining cloud-based frontier models with on-premises systems running open-weight models. He described a policy-based routing approach in which workloads could be directed based on performance, latency, security and cost considerations.
“Enterprise are infinitely hybrid, and we believe that will continue,” McNamara said.
Shift Toward Rack-Scale AI Systems
McNamara said AMD has shifted from managing individual product roadmaps to coordinating a data-center roadmap spanning CPUs, GPUs and networking. The company’s principal focus is now delivering rack-scale solutions, he said.
Ramsay said AMD can approach major computing customers as a broader high-performance-computing partner rather than solely as a supplier of a CPU, GPU, FPGA or semi-custom component. That approach can extend across endpoint devices, edge inference, on-premises servers, cloud deployments, large-scale AI data centers and PCs, he said.
The executives reiterated growth targets previously outlined by AMD, including more than 60% compound annual growth for the data-center business and more than 80% compound annual growth for its AI business over the company’s strategic timeframe. Ramsay said AMD has described a total addressable market of roughly $2 trillion by 2030, representing a 40% growth rate, and expects to grow faster than that market.
Ramsay also pointed to AMD’s expectation of more than doubling its data-center business by 2027. He said the company is focused on scaling its AI business through rack deployments, including Helios systems and MI455 products.
Agentic AI Seen Increasing CPU Demand
McNamara said agentic AI changes the system architecture compared with earlier prompt-response inference applications. Rather than a largely linear deployment of web servers, application servers, databases, storage and GPU servers, agentic systems involve continuous activity, multiple agents and tasks such as control-plane operations, application programming interface calls, database queries and tool execution.
Those tasks are CPU-based, he said, creating demand for both a new class of CPUs used in agentic environments and traditional general-purpose servers that support increased system activity.
AMD is targeting three CPU deployment areas with its Venice portfolio, according to McNamara:
- High-core-count processors for agentic AI environments, including a 256-core device.
- High-frequency processors for head-node workloads that keep GPUs supplied with work.
- General-purpose server processors for broader enterprise and cloud deployments.
Ramsay said AMD increased its estimate of the server CPU total addressable market from $60 billion to $120 billion and then to $220 billion through 2030. He added that the company expects its server business to address more than half of that market, though he did not provide a detailed forecast for market share.
McNamara said CPU content in a gigawatt-scale AI deployment cannot be expressed as a fixed ratio because it varies by customer workload, including the mix of training, inference, general-purpose computing and agentic applications. Still, he said the CPU-to-GPU ratio is growing from what he characterized as roughly one-to-one today.
x86 Ecosystem and Execution Focus
McNamara said the competition between x86 and Arm-based server products is primarily a matter of optimization rather than instruction-set architecture. He argued that the x86 software ecosystem remains important for the large installed base of general-purpose server workloads.
He said AMD sees strong demand for its Turin products and expects Venice to have the company’s broadest launch to date across original equipment manufacturers, original design manufacturers, cloud providers and independent software vendors.
Looking ahead, McNamara said investors may increasingly view AMD as a software and systems company as the company expands its rack-scale capabilities and develops a software stack intended to shorten customers’ time to value. Ramsay said management remains focused on execution as it seeks to scale data-center revenue and grow gross-margin dollars faster than expenses.
About Advanced Micro Devices (NASDAQ:AMD)
Advanced Micro Devices, Inc (NASDAQ: AMD) is a semiconductor company that designs high-performance computing, graphics and adaptive-processing products. Its portfolio includes central processing units (CPUs), graphics processing units (GPUs), accelerated processing units (APUs), field-programmable gate arrays (FPGAs) and adaptive system-on-chip products.
AMD serves customers across data centers, cloud computing, personal computers, gaming consoles, embedded systems and industrial applications.
