AI has moved decisively from experimentation into production, and the defining challenge is no longer building models, but running them at scale. A new eBook from Futurum Research, sponsored by Lenovo, examines the enterprise infrastructure requirements for AI inferencing and the strategic stakes involved.
Futurum estimates the global AI inference market will grow from $5 billion in 2024 to $48.8 billion by 2030, a CAGR of 46.3% with hybrid and edge deployments growing even faster at 65% CAGR. Yet organizations face real obstacles: from memory bandwidth constraints and power density challenges to cost management, talent gaps, and data sovereignty requirements.
The report outlines why general-purpose infrastructure consistently falls short for inference workloads, what specialized hardware and software stacks look like at scale, and how vendors like Lenovo, with its Neptune liquid cooling technology, ThinkSystem platforms, and end-to-end AI advisory services, are helping enterprises move from pilot to production with confidence.
Read below to find out more.