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Batch dispatch

As explained in Sutskever's List

Batch dispatch is a serving technique that groups incoming requests for coordinated processing while controlling response latency.

Batch dispatch groups concurrent audio streams so a speech-recognition model can process requests together rather than separately. Deep Speech 2 (DS2) used it to make GPU-based cloud transcription more practical when the model was too large for on-device use. section 4.4.3

Even batches of two to four streams could reuse loaded model weights, reducing memory-bandwidth overhead. DS2 often achieved 50–70 millisecond latency under moderate load; later accounts describe median latency below 70 milliseconds. These figures show the deployment goal, not a guarantee for every workload. section 4.4.3 section 4.4.4

How Sutskever's List covers it

How deep it goes
Explains concurrent stream grouping and weight reuse, but gives no dispatch algorithm or queueing policy.
Examples
Uses DS2 batches of two to four audio streams and reports 50–70 millisecond latency under moderate load.
What you can do after
Understand how grouping requests can reduce memory-bandwidth overhead while supporting low-latency speech recognition.
How it connects
Connects batch dispatch to speech recognition, GPU inference, and memory bandwidth.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Sutskever's List. The section links point to the parts of the book it draws on.