Runtime Orchestration

Adaptive inference on unpredictable hardware

Real users do not arrive with standardized developer machines. They arrive with old iGPUs, half-full RAM, browser extensions, laptop thermals, and a recording that still needs a transcript. That is why browser AI architecture should be built around uncertainty.

A reliable inference engine asks practical questions in sequence: does the browser expose acceleration, does the device have enough headroom for the target model, has this backend already failed once in the current session, and what is the smallest step down that preserves useful output? Products that skip these questions often confuse “best benchmark” with “best user result.”

Fallback is not an embarrassment

Fallback is what makes the system honest. The browser is not a datacenter. When the accelerated path is weak, a smaller model or a WASM backend is often the correct move. The user cares that the transcript arrives, not that the engineering team insisted on the fanciest path available in theory.

The scheduler becomes part of the product

In a browser transcription app, orchestration logic shapes user trust. If the tool adapts intelligently, users feel the product is working with their machine. If it forces one brittle plan, every failure feels arbitrary. Adaptive inference is less about cleverness than about respecting heterogeneity.