The AI Inference Stack score evolution

Companies designing specialized hardware and system architectures that make AI inference faster, more power-efficient, and more economical at scale.

Software that turns trained models into efficient executable workloads by adapting them to the underlying hardware and improving how inference is scheduled and run.

Platforms that help teams deploy and operate models as reliable production services while handling scaling and infrastructure complexity.

Infrastructure that connects applications with model providers and provides a shared control layer for production inference.

Infrastructure built to run AI models directly on user-owned or edge hardware rather than relying primarily on remote cloud inference. These platforms make local models easier to deploy and operate while accounting for the constraints of the underlying device.