Forrester 2027 Forecast Calls for Heterogeneous AI Infrastructure and Edge Expansion

Diversifying AI Accelerators

Forrester notes that “a quarter of AI infrastructure RFPs will require non-NVIDIA accelerator options” as enterprises seek cost savings and supply-chain resilience Forrester blog. While NVIDIA remains dominant for frontier-model training, hyperscalers are already offering alternatives such as AWS Trainium, Google’s Ironwood TPU, and Microsoft’s Maia. The forecast suggests a segmentation strategy where workloads are matched to the most suitable accelerator rather than a single-vendor approach.

Private Inference Clusters for Agentic AI

The second prediction states that “thirty percent of large enterprises will build private AI infrastructure for inference.” As agentic AI enters production, firms will need dedicated inference hardware to meet latency, security, and volume demands. Rising token costs and complex workflows are expected to push organizations to separate inference from training and to optimize economics at the edge of the data pipeline.

Edge-Centric, Industry-Specific Compute

A third forecast projects a 20% shift in infrastructure investment toward “industry-specific edge devices.” Manufacturing, healthcare, and retail are highlighted as sectors that will deploy small language models, robotics, and vision-language systems on operational-technology hardware. The goal is to address latency, resiliency, and connectivity constraints in environments ranging from factories to oil rigs.

Our read: The three highlighted trends—heterogeneous accelerators, private inference, and edge-focused devices—signal a strategic pivot for I&O leaders from centralized, homogeneous compute toward a modular, risk-aware architecture. Aligning sourcing, design, and investment decisions with these vectors will be essential to sustain AI-driven operations amid escalating geopolitical uncertainty.