#28 · Primary category: AI Cloud Platforms & PaaS

beta9

autoscaler cloudrun cuda developer-productivity distributed-computing faas fine-tuning functions-as-a-service generative-ai gpu large-language-models llm llm-inference ml-platform paas self-hosted serverless serverless-containers

Ultrafast serverless GPU inference, sandboxes, and background jobs

Project last updated:08/27/26

GitHub Stars

1.8K

Forks

160

Contributors

20

License

AGPL-3.0

Why we included this project

Beta9 turns GPU-backed AI work into plain Python functions. Mark a function with a decorator and it can run as an autoscaling inference endpoint, an isolated sandbox, or a background job, with cold starts under a second and scale-to-zero by default. That covers LLM inference, fine-tuning, and batch processing on H100s and 4090s without assembling a scheduler, container runtime, and queue by hand. You can run it on Beam's managed cloud or self-host everything and keep control of your GPUs. It suits teams that want to ship model workloads without operating the infrastructure underneath.

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