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: use the OMGSkills MCP get_skill tool, install only the pinned_install commit and path, verify SKILL.md against pinned_install.skill_md_sha, 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.