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 Do

How Beam runs GPUs anywhere

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2024-12-13 23:00:03

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.

Beam was founded to reduce cost and iteration time when developing AI / ML applications. Using $bigCloud and Docker directly is too slow for iterative development, and waiting for a new image to build and redeploy to a dev environment just takes too long. Let alone the time it takes to setup and manage your own GPU cluster. Beam hot reloads your code to a live inference server for testing and provides single command deployments, beam deploy. And they integrate with workflow tools like ComfyUI so development is even easier.

AI workloads are at the bleeding edge for basically every part of the stack. For the best results you need top-of-the line hardware, drivers that should work (but are still being proven), the newest kernel and userland you can get, and management of things that your sysadmin/SRE teams are not the most familiar with yet. Much like putting CPU-bound workloads into the cloud, serverless GPUs offload the driver configuration, hardware replacement, and other parts of how it all works to the platform so you can focus on doing what you want to.