Presented by AMD
As AI adoption accelerates, businesses are encountering compute bottlenecks that extend beyond just raw processing power. The challenge is not only about having more compute; it’s about having smarter, more efficient compute, customized to an organization’s needs, with the ability to scale alongside AI innovation. AI models are growing in size and complexity, requiring architectures that can process massive datasets, support continuous learning and provide the efficiency needed for real-time decision-making.
From AI training and inference in hyperscale data centers to AI-driven automation in enterprises, the ability to deploy and scale compute infrastructure seamlessly is now a competitive differentiator.
“It’s a tall order. Organizations are struggling to stay up-to-date with AI compute demands, scale AI workloads efficiently and optimize their infrastructure,” says Mahesh Balasubramanian, director, datacenter GPU product marketing at AMD. “Every company we talk to wants to be at the forefront of AI adoption and business transformation. The challenge is, they’ve never been faced before with such a massive, era-defining technology.”
Launching a nimble AI strategy
Where to start? Modernizing existing data centers is an essential first step to removing bottlenecks to AI innovation. This frees up space and power, improves efficiency and greens the data center, all of which helps the organization stay nimble enough to adapt to the changing AI environment.
“You can upgrade your existing data center from a three-generation-old, Intel Xeon 8280 CPU, to the latest generation of AMD EPYC CPU and save up to 68% on energy while using 87% fewer servers3,” Balasubramanian says. “It’s not just a smart and efficient way of upgrading an existing data center, it opens up options for the next steps in upgrading a company’s compute power.”
And as an organization evolves its AI strategy, it’s critical to have a plan for fast-growing hardware and computational requirements. It’s a complex undertaking, whether you’re working with a single model underlying organizational processes, customized models for each department or agentic AI.
“If you understand your foundational situation – where AI will deployed, and what infrastructure is already available from a space, power, efficiency and cost perspective – you have a huge number of robust technology solutions to solve these problems,” Balasubramanian says.
Beyond one-size-fits all compute
A common perception in the enterprise is that AI solutions require a massive investment right out of the gate, across the board, on hardware, software and services. That has proven to be one of the most common barriers to adoption — and an easy one to overcome, Balasubramanian says. The AI journey kicks off with a look at existing tech and upgrades to the data center; from there, an organization can start scaling for the future by choosing technology that can be right-sized for today’s problems and tomorrow’s goals.
“Rather than spending everything on one specific type of product or solution, you can now right-size the fit and solution for the organizations you have,” Balasubramanian says. “AMD is unique in that we have a broad set of solutions to meet bespoke requirements. We have solutions from cloud to data center, edge solutions, client and network solutions and more. This broad portfolio lets us provide the best performance across all solutions, and lets us offer in-depth guidance to enterprises looking for the solution that fits their needs.”
That AI portfolio is designed to tackle the most demanding AI workloads — from foundation model training to edge inference. The latest AMD InstinctTM MI325X GPUs, powered by HBM3e memory and CDNA architecture, deliver superior performance for generative AI workloads, providing up to 1.3X better inference performance compared to competing solutions1,2. AMD EPYC CPUs continue to set industry standards, delivering unmatched core density, energy efficiency and high-memory bandwidth critical for AI compute scalability.
Collaboration with a wide range of industry leaders — including OEMs like Dell, Supermicro, Lenovo, and HPE, network vendors like Broadcom and Marvell, and switching vendors like Arista and Cisco — maximizes the modularity of these data center solutions. It scales seamlessly from two or four servers to thousands, all built with next gen Ethernet-based AI networking and backed by industry-leading technology and expertise.
Why open-source software is critical for AI advancement
While both hardware and software are crucial for tackling today’s AI challenges, open-source software will drive true innovation.
“We believe there’s no one company in this world that has the answers for every problem,” Balasubramanian says. “The best way to solve the world’s problems with AI is to have a united front, and to have a united front means having an open software stack that everyone can collaborate on. That’s a key part of our vision.”
AMD’s open-source software stack, ROCmTM, is widely adopted by industry leaders like OpenAI, Microsoft, Meta, Oracle and more. Meta runs its largest and most complicated model on AMD Instinct GPUs. ROCm comes with standard support for PyTorch, the largest AI framework, and has more than a million models from Hugging Face’s premium model repository enabling customers begin their journey with seamless out of the box experience on ROCm software and Instinct GPUs.
AMD works with vendors like PyTorch, Tensorflow, JAX, OpenAI’s Triton and others to ensure that no matter what the size of the model, small or large, applications and use cases can scale anywhere from a single GPU all the way to tens of thousands of GPUs — just as its AI hardware can scale to match any size workload.
ROCm’s deep ecosystem engagement with continuous integration and continuous development ensures that new AI functions and features can be securely integrated into the stack. These features go through an automated testing and development process to ensure it fits in, it’s robust, it doesn’t break anything and it can provide support right away to the software developers and data scientists using it.
And as AI evolves, ROCm is pivoting to offer new capabilities, rather than locking an organization into one particular vendor that might not offer the flexibility necessary to grow.
“We want to give organizations an open-source software stack that is completely open all the way from the top to the bottom, and all the way across an organization,” he says. “Users can choose the layers that meet their needs and modify them as necessary, or run models right out of the box, ensuring that enterprises can run intensive models like DeepSeek, Llama or the latest Gemma models from Google from day one.”
A look ahead: AMD’s vision for AI compute
As organizations embrace AI’s early revolution, they need to avoid getting locked into one particular solution, finding compute solutions that meet their needs now and in the future. Working with an industry expert is critical to identifying those needs, and what is required to carry them forward as AI changes the world.
AMD is driving that change, collaborating with leading AI labs at the forefront of AI development, and the broader ecosystem of developers and leading software companies. With a growing customer base that includes Microsoft, Meta, Dell Technologies, HPE, Lenovo and others, AMD is shaping the AI landscape by providing high-performance, energy-efficient solutions that drive innovation across industries
Looking ahead, that collaboration is foundational to AMD’s technology roadmap. The company is investing in comprehensive hardware and software solutions, including the recent acquisition of ZT Systems, bringing essential server & cluster design expertise to bring full-stack solutions to market fast with our OEM, ODM and Cloud partners.
And as models become larger and more sophisticated, hardware demands are increasing exponentially. This is what drives AMD’s product strategy and feature sets: to ensure its portfolio of solutions can scale, with open and flexible AI infrastructure that maintains performance and efficiency.
“This broad portfolio is designed to right-size AI solutions to provide the best performance across every customer setup and power AI strategies of every size,” Balasubramanian says. “No matter which part of the AI journey an organization is on, whether they’re building a model or using a model for an end use case, we’d like for them to come and talk to us, and learn how we can help solve their biggest problems.”
New AMD Instinct MI325X Accelerators are breaking the boundaries of AI performance — learn more now.
Footnotes1. https://www.amd.com/en/legal/claims/instinct.html#q=MI325-014
2. https://www.amd.com/en/legal/claims/instinct.html#q=MI325-015
3. https://www.amd.com/en/legal/claims/epyc.html#q=SP9xxTCO-002A
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