Cold Starts, Warm Runs and Real Latency

Wed, 02 Sep 2026 15:30:00 +0100

“Local AI is faster” is not a measurement.

It compresses several different waiting periods into one adjective. A browser-native feature can avoid network round trips and still make a user wait for model acquisition, process startup, session creation, context ingestion or slow generation.

To understand latency, we have to take the lifecycle apart.

1. One duration hides several clocks

For one interaction, useful timestamps include:

Phase	Starts	Ends	User-visible?
Capability inspection	`availability()` call	state returned	Usually not
Acquisition	session request requires assets	assets ready	Yes, if it blocks
Session creation	`create()` called	session returned	Often
Prompt startup	prompt called	first chunk	Yes
Generation	first chunk	final chunk	Yes
Validation	output complete	feature accepted/rejected	Sometimes

The perceived wait for a streaming feature is often dominated by time to first output:

Cold Start on Programmer.ie: Modern AI programming

Cold Starts, Warm Runs and Real Latency

1. One duration hides several clocks