2 posts tagged with "case study"

What do you do when you need to serve up a completely custom, 7+ billion parameter model with sub 10 second cold start times? And without writing a Dockerfile or managing scaling policies yourself. It sounds impossible, but Beam's serverless GPU platform provides performant, scalable AI infrastructure with minimal configuration. Your code already does the AI inference in a function. Just add a decorator to get that function running somewhere in the cloud with whatever GPU you specify. It turns on when you need it, it turns off when you don't. This can save you orders of magnitude over running a persistent GPU in the cloud.

fal.ai’s team set an ambitious goal: host the fastest diffusion inference endpoints in the world without passing the bill onto their users. Their platform needed to remain affordable for individual developers, all while ingesting 10s of TBs in mere hours, storing 100+ TBs of data around the globe, and offering real time responses.