Running AI on mixed hardware for speed and affordability

This IBM Research article discusses optimizing AI inference performance and costs by leveraging mixed hardware configurations, combining different types of processors and accelerators rather than relying on a single hardware type. The approach aims to balance computational speed with economic efficiency for deploying machine learning models in production environments. The research likely covers techniques for distributing AI workloads across heterogeneous hardware to achieve better performance-per-dollar metrics.

Source: IBM Research | 2026-06-23