Performance
What performance means in a browser AI transcription tool
Performance is not only speed. In a browser setting it also includes startup cost, memory headroom, long-run stability, and whether the session survives a realistic queue.
Main factors
Model size
Larger models demand more memory and can amplify instability on constrained devices.
Quantization
Smaller representations often improve feasibility and completion rate in the browser.
Backend choice
WebGPU can accelerate throughput, but WASM may still be the safer path on some machines.
Job length
Short demos and multi-hour media expose very different runtime behavior.
Performance guidance
If the browser feels fragile, step down in model size before blaming the concept of local AI itself. A smaller model that completes cleanly is usually more valuable than a larger one that spends its time reallocating memory or failing halfway through a long job.
For folders and long recordings, queue pacing and cleanup matter as much as raw throughput. That is why batch systems need disciplined resource handling rather than only faster kernels.