The Future of AI Inferencing Lies in Open, Flexible Infrastructure Stacks

The enterprise AI landscape is reaching a decisive tipping point: annual worldwide spending on AI inferencing infrastructure is projected to surpass AI training for the first time. As 60% of organizations turn away from building models from scratch to customizing existing commercial or open-source solutions, the primary operational challenge has shifted from model training to model deployment. With infrastructure hardware accounting for nearly half (47%) of total enterprise AI budgets, the architectural decisions IT leaders make today directly determine their bottom-line return on investment.

This IDC White Paper examines the high-stakes architectural divide between proprietary single-vendor silos and open, interoperable ecosystems. Drawing parallels from past datacenter disruptions—from mainframes and Unix to the rise of Linux—IDC outlines why open infrastructure is positioned to dominate the inferencing era.

Key insights inside this white paper include:

• The ROI Blueprint: How factors such as concurrency, latency, context windows, and model scale dictate infrastructure cost efficiency.
• Distributed Serving vs. AI Factories: Why 79% of organizations require AI embedded directly into general-purpose enterprise applications rather than siloed, batch-driven environments.
• The Open Stack Advantage: A breakdown of major industry stacks—including AMD ROCm, Intel OpenVINO, the UXL Foundation, and NVIDIA—and why cross-architecture portability (CPUs, GPUs, and NPUs) prevents costly vendor lock-in.
• Enterprise Solutions in Practice: How open standards like Ultra Accelerator Link (UALink) and memory-first GPU architectures enable cost-effective scale for large language models and real-time agentic workloads.

Discover how to break free from proprietary constraints, de-risk your deployment pipeline, and build a scalable, future-ready AI infrastructure.

Download the full IDC White Paper to inform your enterprise AI inferencing roadmap.