This article is part of VentureBeat’s special issue, “The Real Cost of AI: Performance, Efficiency and ROI at Scale.” Read more from this special issue.
Presented by AMD
The era of the “AI proof-of-concept” is closing fast as enterprises look to move past dazzling demos of AI’s potential, to production systems that deliver impactful business outcomes.
Yet, as many enterprises have discovered during this recent boom in AI innovation, implementations are rarely seamless. The challenges are most acute in the data center. Designed around transactional workloads, these environments are feeling the strain of new AI workloads where models with billions of parameters are becoming the standard.
Compute, power, cooling, and floor space are all being squeezed, and technology leaders must rethink the data center from the silicon upwards if they’re to accelerate business outcomes with AI and enable sustained success.
Why yesterday’s data center can’t finance tomorrow’s AI
Server consolidation is the first lever they should reach for in order to prepare for AI faster.
CPU resources, storage, and network bandwidth are already operating at, or near, full capacity. But simply adding more racks of AI-capable hardware isn’t the answer when data center floor space is also in short supply, and power and cooling demands remain concerningly high. Room for maneuver is further limited by the way sunk costs in maintaining ageing gear diverts funds and people from more transformational work. Through this lens, this infrastructure debt is also a tax on AI innovation, slowing readiness initiatives and creating risks to long-term scalability.
Latest-generation CPUs carry dozens — and often hundreds — of cores per socket, offering massive parallelism for efficient data pre-processing and powerful everyday inference tasks. One AMD EPYC™-based server, for example, can replace seven older boxes.9xx5TCO-002a This frees precious floor space for AI-specific clusters and advanced cooling, while higher performance-per-watt slashes power draw and therefore ongoing operational costs. In fact, organizations can anticipate consolidation-driven savings to fund subsequent AI investments.
Train for the marathon and the sprint
Navigating enterprise AI adoption and the underlying data center infrastructure modernizations is a sprint within a marathon. AI tooling is evolving at a much faster pace — months, even weeks — than infrastructure lifecycles spanning years.
Enterprises can get out of the blocks fast with the near-term performance gains that come with server consolidation. But turning that strong start into a commanding lead will require a flexible, scalable infrastructure that shortens AI deployment timelines, avoids expensive code rewrites and allows them to take future AI breakthroughs in their stride.
The key to this kind of sustained success is selecting the right compute for the right AI workload. Deep learning is highly data intensive and demands the greater memory bandwidth and parallel processing of GPUs. At a smaller scale, most inference tasks can comfortably be handled using the higher computational efficiency and task orchestration strengths of CPUs. But combining the strengths of CPUs with GPU’s parallel power offers a pathway to handle the largest models and meet expanding AI demands.
Where new infrastructure is required, AI scale-outs will also be simpler for enterprises that opt to stick with x86 architectures over ARM-based options, thus allowing existing x86 applications to be retained. Organizations can further accelerate deployment timelines by leveraging pre-optimized libraries, containers, and reference implementations that run quickly on their chosen infrastructure.
Confidential computing is now a baseline requirement
As AI becomes embedded across the enterprise it will naturally touch more data, amplifying the security stakes. Achieving high-performance AI often relies on heterogeneous hardware (CPUs, GPUs, and specialized accelerators), spread across multiple nodes and even multiple sites. Ensuring a secure, trusted boundary across all devices and network links is nontrivial. Even if data is encrypted at rest, vulnerabilities in virtualized or containerized environments may allow malicious hypervisors to access sensitive information.
This makes confidential computing a baseline requirement for enabling sustained success with AI, and hardware-level protections such as Secure Encrypted Virtualization (SEV) keep models and data encrypted even in memory, creating a trusted boundary across heterogeneous clusters. Silicon-level security features can extend that protection to I/O paths and help contain insider or hypervisor threats.
Power up AI with partnerships
Strategic clarity on everything from silicon to software to security — not just the hard measure of capital investment — will be what separates AI leaders from laggards in the next phase of enterprise adoption. And the key to gaining that clarity will be choosing the right partners with the right ecosystems.
A vendor with a broad end-to-end AI compute portfolio and deep alliances across OEMs, CSPs, and ISVs will streamline integrations and de-risk future pivots. Equally important is a vendor’s track record of turning PoCs into production at scale. Meanwhile, proven execution against product roadmaps protects the ROI of strategic bets stretching over five years or more.
This time of enterprise data-center resets will be bold and far reaching. It’s a multiyear journey covering modernizing CPUs; designing flexible, scalable hybrid CPU/GPU architectures, embedding hardware-rooted security and choosing partners, like AMD, with both portfolio depth and roadmap discipline.
By making these decisions today, technology leaders can establish a future-ready infrastructure that adds long-term value at the same aggressive pace that it runs AI models.
AMD powers the full spectrum of AI, bringing together leadership CPUs, GPUs, and other accelerators, networking, and open software to deliver unmatched flexibility and performance. Watch the Advancing AI 2025 keynote here to learn more.
Ravi Kuppuswamy is SVP Server Product & Engineering at AMD.
Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact
