AI hardware strategies for scaling infrastructure efficiently

AI infrastructure spending is projected to triple to $100 billion annually by 2028, driven by new purpose-built hardware designed specifically for AI workloads.

Market momentum: Enterprise spending on AI-specific compute and storage infrastructure grew 37% year-over-year in early 2024, marking 18 consecutive months of double-digit growth.

• IDC forecasts that dedicated and public cloud infrastructure will represent 42% of new AI spending worldwide through 2025
• Organizations currently have more projects in pilot (10) and limited deployment (16) phases compared to full-scale deployment (6)
• Infrastructure limitations remain a key barrier to scaling AI initiatives beyond initial pilots

Cloud-first approach: Most enterprises should leverage AI hardware capabilities through cloud services rather than direct purchases.

• Cloud providers and hyperscalers are acquiring the majority of new AI-specific hardware
• Cloud services offer faster deployment, simplified scaling, and pay-as-you-go pricing models
• New specialized cloud services incorporating AI-optimized hardware are expected throughout 2025

DIY considerations: Self-built AI infrastructure makes sense for select enterprises with specific characteristics.

• Best suited for financial services, healthcare, and other regulated industries with substantial budgets
• Requires significant capital expenditure and specialized technical expertise
• Can be justified when operational cost savings exceed 20-30% over a three-year period

Enterprise hardware evolution: More affordable AI-ready hardware options are emerging for typical organizations.

• Major vendors are introducing integrated AI infrastructure packages aimed at enterprises
• New AI-capable PCs and edge devices are expanding deployment options
• Gartner predicts all new enterprise PCs will be AI-ready by end of 2026

Strategic priorities: Success with AI infrastructure requires careful attention to foundational elements.

• Organizations must clearly define business use cases and ROI metrics before selecting hardware
• Data quality and management capabilities are essential prerequisites
• Energy consumption and cooling requirements need careful consideration
• Software orchestration capabilities must match hardware investments

Future considerations: The AI infrastructure landscape remains dynamic with several key trends emerging.

• AI workloads are increasingly shifting from centralized training to edge inference
• Multi-cloud strategies help avoid vendor lock-in
• Open standards and decoupled architectures provide flexibility for adopting new technologies
• Energy efficiency and sustainability concerns will influence infrastructure decisions

Looking ahead: While specialized AI hardware will continue expanding rapidly, organizations should maintain flexibility in their infrastructure strategies to adapt to evolving technologies and requirements.