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AI Inference: Enterprise Infrastructure and Strategic Imperatives

AI Inference: Enterprise Infrastructure and Strategic Imperatives

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Artificial intelligence has entered its production phase. While foundation-model training gets the headlines, the real economic value is created through inferencing—deploying trained models to make predictions, generate responses, and drive day-to-day business decisions across the enterprise.

As organizations move from pilots to production, inference infrastructure becomes a strategic choice, not a tactical one. Enterprises must balance latency, cost-per-inference, power density, and data sovereignty across cloud, on-premises, and edge deployments—while avoiding “bill shock,” performance bottlenecks, and operational fragility.

In our latest Market Report, AI Inference: Enterprise Infrastructure and Strategic Imperatives, completed in partnership with Lenovo, Futurum Research examines the rapid growth of AI inference, the shift toward hybrid and edge architectures, and the technical requirements for building reliable, cost-efficient inference at scale—along with a practical framework for evaluating solutions.

In this report, you will learn:
  • Why AI inference infrastructure is projected to grow from $5.0B in 2024 to $48.8B by 2030—and what that means for enterprise investment priorities
  • How and why hybrid and edge inference are accelerating (65% CAGR), reshaping how organizations place and operate inference workloads
  • The most common business, operational, and technical bottlenecks (e.g., cost management, talent gaps, memory bandwidth saturation, and Time to First Token requirements)
  • What “specialized” inference infrastructure looks like across compute, memory, networking, cooling/power, and the software stack (optimization, runtimes, orchestration, observability)
  • How to evaluate AI inference solutions using a consistent framework (performance validation, scalability, TCO, flexibility, and security/compliance)
To learn more about the AI inference infrastructure market outlook, hybrid/edge shift, and the technical requirements for scaling inference, download AI Inference: Enterprise Infrastructure and Strategic Imperatives today.

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Author Information

Nick Patience is VP and Practice Lead for AI Platforms at The Futurum Group. Nick is a thought leader on AI development, deployment, and adoption - an area he has researched for 25 years. Before Futurum, Nick was a Managing Analyst with S&P Global Market Intelligence, responsible for 451 Research’s coverage of Data, AI, Analytics, Information Security, and Risk. Nick became part of S&P Global through its 2019 acquisition of 451 Research, a pioneering analyst firm that Nick co-founded in 1999. He is a sought-after speaker and advisor, known for his expertise in the drivers of AI adoption, industry use cases, and the infrastructure behind its development and deployment. Nick also spent three years as a product marketing lead at Recommind (now part of OpenText), a machine learning-driven eDiscovery software company. Nick is based in London.

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