Why do AI workloads require specialized accelerators instead of standard CPUs?

AI workloads — particularly model training and inference — involve massive parallel processing tasks that standard CPUs are not designed to handle efficiently. AI accelerators like GPUs and NPUs are purpose-built to execute these operations at the speed and scale modern AI applications demand. Without them, data center performance bottlenecks can severely limit AI capabilities.

Does it matter whether a business chooses on-premises or cloud infrastructure for AI?

Both deployment models are viable, but each comes with distinct trade-offs around latency, data sovereignty, cost, and access to cutting-edge accelerator hardware. Cloud providers offer rapid access to the latest AI chips without capital expenditure, while on-prem gives organizations greater control over sensitive data and long-term cost structures. The right choice depends on the organization's specific workloads, compliance requirements, and strategic priorities.

What should businesses do now to prepare their infrastructure for AI demands?

Organizations should conduct an audit of their current compute infrastructure to identify gaps between existing capabilities and what their AI roadmap will require. They should evaluate accelerator options — both cloud-based and on-prem — and factor in power, cooling, and networking upgrades that high-density AI hardware typically demands. Building a flexible, scalable strategy now can prevent costly retrofits as AI adoption accelerates.

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On-Prem or Cloud? Either Way, Data Center Compute Demands Necessitate the Need for AI Accelerators

Businesses must rethink their infrastructure strategy as AI workloads reshape what data centers can actually deliver

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By Mark Beccue · May 14, 2024, 10:25 AM UTCAi AcceleratorsData Center ComputeData CentersExperts Talk

Key takeaways

AI workloads are outpacing what conventional data center compute can deliver, making AI accelerators a necessity rather than an option.

The on-prem vs. cloud debate is no longer just about cost or control — it now hinges on access to specialized AI compute hardware.

Organizations must proactively reassess their infrastructure roadmaps to align with the realities of accelerated AI computing demands.

With the increasing demand for powerful data center compute capabilities, data centers are at the forefront of driving the need, development, and purchase of AI accelerators. This surge is influenced by the expanding role of AI in business operations, creating a critical demand for advanced hardware solutions.

Data centers are at the forefront of driving the need, development, and purchase of AI accelerators.

How are data centers shaping the landscape of AI compute, and what strategic decisions should businesses consider in this evolving environment?

In a recent Experts Talk roundtable discussion, Mark Beccue, a top AI market research analyst, expanded on the nuances of data center compute and the pivotal role of AI accelerators. His expert analysis offers valuable insights into the current trends and strategic considerations for enterprises navigating this complex terrain.

Main Takeaways from Mark Beccue's Analysis:

Data Center Compute: Heavy computational loads handled by data centers fall into two categories: on-premises (on-prem) and cloud-based solutions
Enterprise Decisions: Enterprises must choose between investing in on-prem data centers or leveraging cloud services from providers like Amazon, Azure, and Google
Pros and Cons: Cloud services offer quick deployment and scalability, but data security remains a significant concern for many companies
Market Trends: The limited availability of compute resources in the market drives strategic decisions about whether to build on-prem from scratch or rely on cloud services
Strategic Considerations: Enterprises need to weigh the benefits of speed and scale against the challenges of data security and management when choosing their AI infrastructure

For a more in-depth discussion, view the complete roundtable conversation.

Video TranscriptExpand ↓

A little bit is, I'm gonna focus back on what's called data center, you know, compute. So that's the really heavy load computes. What you have is cloud versus on prem. And, so you have a lot of enterprises that have their own data centers, and they they call that on prem. And they and so you ask where these things get bought. The those all of these sophisticated accelerators get bought in the data centers either on prem, so that's an enterprise buying it themselves, or by the cloud players being, you know, the Amazons, the, Azures, you know, Google, and those types of things. And what there's a debate, I guess. You know, you can say, it's not a real great time to start building your on prem if you wanna start from scratch. But, you know, compute is a a a narrow it's a limited commodity right now, I think, we're seeing in the marketplace. So a lot of enterprises are making decisions. If they don't have on prem, they're everybody's thinking about the cloud. Is this a good idea or not? You know, there are pros and cons to those things. The pros being you're up and running much quicker. There's some scale to that. The cons being, you know, how you handle your data. Security is always an issue for lots of companies, but that's where I would say is kinda one of the bigger challenges around what we're talking about.

About the author

Mark BeccueResearch Director

Mark Beccue is a veteran market research analyst with more than 25 years of experience in market research and business strategy. Mark is one of a handful of pioneering analysts who began to focus on AI market research in 2015. Today with Futurum Group and previously as a principal analyst within the AI practice area at Omdia and Tractica, he has advised clients and provided them with syndicated and custom qualitative AI research services. His expertise in AI use cases, applications and software, natural language AI and broader trends surrounding AI market adoption have made him a well-known and sought after speaker, panel moderator, conference chair and media resource within the AI business community. He has served in those roles for events including the AI Summits in London, Singapore, New York and Silicon Valley, IOT World, Smart Home Summit, UX Next and Telco AI Europe. Prior to joining Tractica, Mark was an independent market research analyst focused on emerging technologies. Before going independent, Mark served as in house market intelligence analyst for Syniverse, where he helped guide overall business and product line strategies. For 4 years Mark worked as a Senior Analyst for ABI Research, a global technology research firm, focusing on mobile consumer services. Prior to ABI, Mark worked for 10 years for Syniverse in product management, greenhouse innovation and marketing. Specialties: AI B2B and B2C market intelligence, analysis and insights. Natural Language AI. Operationalizing AI in the Enterprise. AI market adoption trends and issues. Strengths - AI and other technical market analysis designed for business readers, writing, thought leadership, forecasting, market sizing

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