The shift from training AI models to serving them is creating a heterogeneous landscape for inference where hardware specialization is becoming critical. Companies like Nvidia and AWS are disaggregating compute paths, using different chips for prefill and decode operations to maximize efficiency. Meanwhile, startups like Lumai are introducing optical inference accelerators that use light instead of electricity to process massive matrix multiplications.