DATASET + REPORT
2H 2025 Data Center Semiconductors Global Enterprise Decision Maker Survey Report

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Futurum Intelligence has released an insight-rich look into how 831 global IT decision-makers are reshaping their data center strategies as AI moves from experimentation to large-scale production. It reveals a market in transition — where GPUs still dominate but alternatives like XPUs are gaining traction, inference is becoming the true driver of AI investment, and the biggest barriers to growth stem not from budgets but from physical limits such as power, cooling, and networking.

With buyers demanding more openness, flexibility, and efficiency across hybrid deployments, the findings illuminate the real-world challenges and emerging opportunities that will define the next generation of AI infrastructure.

It’s an essential read for anyone interested in where enterprise AI compute is headed—and which vendors are best positioned to lead the next phase.

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SNEAK PEEK INSIDE THE REPORT
Data Center Semiconductors Global Enterprise Decision Maker Survey

  • GPUs dominate today, but diversification is slowly emerging.
    Enterprises rely heavily on GPUs for AI workloads, yet early traction in XPUs indicates a growing appetite for alternatives as cost, supply, and scalability pressures intensify.
  • Inference is becoming the center of gravity for AI investment.
    Enterprises are shifting from building models to operationalizing them, placing greater emphasis on scaling inference workloads efficiently across environments.
  • Physics — not budget — is the primary barrier to scaling AI infrastructure.
    Power, cooling, and interconnect limitations now outweigh financial constraints, reshaping buying criteria and deployment strategy.
  • Vendor concentration is high, but lock-in tolerance is gradually weakening.
    NVIDIA maintains an overwhelming lead; however, buyers are increasingly signaling demand for flexibility, second sources, and multi-vendor strategies as deployments grow.
  • Hybrid deployment is the operational reality.
    Organizations blend public cloud, colocation, and on-prem environments to balance cost, control, and performance for AI workloads.
  • The definition of performance is evolving.
    Time-to-train and peak throughput remain vital, but efficiency metrics such as cost-per-inference, watts-per-token, and predictable scaling are gaining weight in purchase decisions.

Exclusive access to these details and more is available in the Futurum Intelligence Platform.