cerebrium
Use for any Cerebrium task: deploying Python code to serverless GPU or CPU, writing or fixing cerebrium.toml, choosing hardware and regions, calling deployed endpoints (REST, streaming, WebSocket, async), autoscaling and concurrency, cold starts, secrets, CI/CD, and debugging a build or a running app from the terminal. Covers the cerebrium CLI, configuration defaults the API actually applies, accepted GPU identifiers with per-plan limits, and troubleshooting.
Install
With your agent
Install OMGSkills skill "cerebriumai/cerebrium-skills:skills/cerebrium" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/CerebriumAI/cerebrium-skills /tmp/cerebrium-skills && ln -s /tmp/cerebrium-skills/skills/cerebrium ~/.claude/skills/cerebriumFrom README
Cerebrium Cerebrium runs Python workloads on serverless GPU and CPU: REST endpoints, SSE streaming, WebSockets, and async jobs, with scale-to-zero and per-second billing. One cerebrium.toml describes hardware, scaling, dependencies and runtime; one CLI (cerebrium) drives everything. Reach for it when the workload is an inference API for an LLM, an embedding model or a vision model; a real-time voice, video or streaming app; bursty traffic that should scale from zero without holding idle GPUs; a deployment that has to run in several regions for latency or data residency; or a migration off Replicate, Hugging Face or Mystic, each of which has a guide under https://cerebrium.ai/docs/migrations. This file carries the workflow and the rules. Load the reference that matches the task: | Read | When | | --- | --- | | references/cli.md | Running any cerebrium command: the full surface, flags, non-interactive auth, CI/CD, which commands cost money.