What it does

This is Cerebrium’s official examples repository — a collection of self-contained, deployable ML projects for their serverless GPU platform. Each folder is a complete recipe: clone, cerebrium deploy, and you have a running endpoint. Categories span getting-started basics, LLM serving with vLLM, voice agents (Whisper, Twilio, LiveKit), image generation (ComfyUI, SDXL variants), and integrations with LangChain and Gradio.

The interesting bit

The breadth is the point. Rather than one polished demo, this is a sprawling reference library — 40+ examples covering batching strategies, Inferentia deployment, WebSocket streaming, even a “build your own OpenAI Realtime API replacement.” It’s documentation by executable example, which is rarer than it should be in MLOps.

Key highlights

• One-command deploys: Each example is structured for cerebrium deploy with minimal configuration
• Voice-heavy: 10 examples including multilingual agents, RAG-powered voice assistants, and Sesame’s CSM model
• Batching as first-class: Dedicated section on LitServe, vLLM, and transformers batching patterns
• Migration path: Includes a COG-to-SDXL migration example for refugees from other platforms
• Swag bribes for contributors: Merged PRs earn physical merchandise (documented, not implied)

Caveats

• Quality variance likely: 40+ examples in one repo, maintained by a small team — some will be fresher than others
• Platform lock-in: All examples assume Cerebrium’s proprietary deployment CLI and infrastructure
• No benchmarks or cost estimates: README mentions “faster inference” but provides no numbers to back it up

Verdict

Worth bookmarking if you’re already on Cerebrium or evaluating serverless GPU platforms — the voice and image generation examples are particularly dense. Skip it if you’re committed to AWS SageMaker, RunPod, or self-managed Kubernetes; the platform-specific deployment code won’t transfer.