As AI adoption accelerates across industries, companies are facing more than just the need for raw processing power. The real challenge is building smarter, scalable compute infrastructure tailored to support the growing complexity of AI models and workloads. Modern AI isn’t just about bigger models — it’s about running massive datasets, supporting continuous learning, and enabling real-time decision-making. From hyperscale data centers to AI-driven automation within enterprises. The ability to adapt compute resources has become a competitive edge. “It’s a tall order,” says Mahesh Balasubramanian, Director of Datacenter GPU Product Marketing at AMD. “Every organization wants to lead in AI innovation, but the scale of what’s needed is unprecedented — and they’re racing to keep up.”
For most businesses, the first step is modernizing aging data center infrastructure. Upgrading to newer processors like AMD EPYC CPUs not only unlocks better performance and energy efficiency. But also frees up power and space for next-gen AI deployments.
“Switching from older Intel Xeon CPUs to the latest AMD EPYC processors can cut energy use by up to 68% while reducing server counts by 87%,” Balasubramanian explains. “It’s not just efficient — it lays the foundation for scalable AI compute.”
This opens the door for companies to right-size their AI strategy, scaling resources based on current needs while keeping an eye on future grow
A common misconception is that AI demands a massive, upfront investment in hardware, software, and services. But Balasubramanian says flexibility is the smarter play.
“You don’t have to bet everything on one solution,” he says. “With AMD’s broad portfolio — spanning cloud, data center, edge, and network — organizations can design bespoke compute strategies that evolve with their AI ambitions.”
From foundation model training to edge inference, AMD’s latest hardware lineup is designed to tackle the toughest AI workloads:
- AMD Instinct™ MI325X GPUs, powered by HBM3e memory and CDNA architecture, deliver up to 1.3X better inference performance for generative AI tasks.
- AMD EPYC CPUs lead the industry in core density, efficiency, and memory bandwidth, all critical for scaling AI compute.
They also partners with OEMs like Dell, Supermicro, Lenovo, and HPE, plus network leaders like Broadcom, Marvell, Arista, and Cisco, to deliver modular solutions. These scale easily — from a handful of servers to thousands — all running on next-gen Ethernet-based AI networking.
While hardware is essential, AMD sees open-source software as the real driver of AI’s future.
“No single company has all the answers,” Balasubramanian says. “True AI innovation will come from collaboration — and that’s only possible with open software stacks.”
At the heart of AMD’s approach is ROCm™, its open-source AI software stack:
- Adopted by leaders like OpenAI, Microsoft, Meta, and Oracle
- Fully compatible with PyTorch, supporting 1M+ Hugging Face models
- Enables organizations to run powerful models like DeepSeek, Llama, or Google’s Gemma out of the box
ROCm is built for seamless scaling — from a single GPU to tens of thousands — matching the hardware flexibility AMD provides.
Its robust CI/CD pipeline ensures new features and updates integrate smoothly, keeping developers and data scientists on the cutting edge without breaking their workflows.
“We’re committed to offering a fully open stack — top to bottom — so customers stay agile as AI evolves,” Balasubramanian adds.
As AI models grow larger and more complex, compute needs will skyrocket. Avoiding vendor lock-in and building for flexibility is critical for businesses navigating this AI revolution.
AMD is positioning itself as a key partner — working with leading AI labs, cloud providers, and software ecosystems to shape the future of AI compute.
With customers like Microsoft, Meta, Dell, HPE, and Lenovo, AMD’s focus remains on delivering high-performance, energy-efficient solutions that empower businesses to innovate.
Their recent acquisition of ZT Systems further boosts AMD’s full-stack capabilities, accelerating time-to-market for AI infrastructure alongside OEM, ODM, and cloud partners.
“Our portfolio is built to right-size AI strategies — offering the best performance at every stage,” Balasubramanian says. “Wherever you are in your AI journey — building models or deploying them — we’re here to help solve your biggest challenges.”
