What heterogeneous computing is (and why it matters for AI)

Most companies running AI inference today rely on a single processing vector: the GPU. It works, but it leaves the rest of the available hardware — CPU, NPU, XPU — idle, and every performance gain ends up depending on buying more GPU.

Heterogeneous computing is the approach of orchestrating, in an integrated way, all the accelerators available in a machine: GPU, CPU, NPU and XPU, from any vendor. Instead of a single processing vector, the workload is distributed across two or more, making use of hardware the company already owns.

The practical impact is twofold. First, more performance per machine, because no accelerator sits idle while the GPU processes alone. Second, less dependency on a single vendor: if the solution isn't tied to one specific chip brand, the company gains freedom to negotiate, migrate and scale without hardware lock-in.

That's why the Linux Foundation created the UXL Foundation, a foundation dedicated to standardizing and promoting heterogeneous computing — and it's why MultiCortex built its entire stack, from the operating system to the end products, on this principle.

In practice, for anyone already running AI in production, this means extracting more tokens per second from the same hardware, reducing computational and energy cost, and keeping operations 100% on-premise or in a private cloud, without depending on a single vendor to keep evolving.