Your AI endpoint is fast. Your first request isn’t.

Start here: https://fandf.co/4pXTLNM

When a serverless function has been idle there’s nothing running, so the platform has to build a fresh execution environment, start the runtime, and load your dependencies before your code runs at all. Serving a model adds a fourth step that dwarfs the other three: streaming the weights into GPU memory. For a 70B model that’s well over 100 seconds before it answers anything.

The usual fixes both have a catch. Keep-alive pings keep one instance warm, so a real traffic spike blows straight past it and the new concurrent requests cold start anyway. Provisioned concurrency fixes that by reserving a pool, which turns a latency problem into a bill you pay whether the capacity gets used or not.

Akamai built their serverless on WebAssembly instead of containers — a module instantiates rather than provisioning an environment per request — and they claim zero cold starts.

Their Edge Case Discord is a good place to take this kind of problem. It’s open to anyone building at the edge, there’s no application process, and Akamai’s own senior developer advocates run live architecture and coding sessions in there: blank canvas, real code, real bugs, deployed to production while you watch. New users get $300 in Akamai Cloud credits for joining.

Paid partnership with Akamai.

#serverless #coldstart #ai #gpu

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