Cerebrium's examples repository is a catalogue of serverless GPU deployments, not a framework

Sixty projects that each deploy with one command

Cerebrium's official examples repository is described as a collection of examples to help you get started with building Machine Learning and AI applications on the platform, covering whether you want to deploy LLMs, process voice data, or handle image and video tasks.

The unit of organisation is a project, not a package. Each example is a self-contained project that demonstrates a specific use case, and you can explore the examples in any order depending on your interests and needs. Each example includes detailed instructions on how to deploy the application on the Cerebrium platform.

The deployment itself is one command.

cerebrium deploy

The README says to deploy each example by cloning the repo and running that command in each example folder. There is no install step for the repository as a whole and no shared library to depend on, and the primary language is listed as Python with the topics covering ai, gpu, llms, ml, mlops, serverless and serverless-functions.

So this is documentation that happens to be executable. For an engineer deciding whether to try a hosted GPU platform, the value is not a dependency but a set of working reference implementations you can read in an afternoon.

The last push to the repository was on 2026-10-14, the default branch is master, and there are no GitHub releases. The homepage is docs.cerebrium.ai.

The eleven categories, and what the numbering reveals

The examples are split into numbered categories, and the numbering tells you how the project grew.

The first three are getting started: deploy your first model, managing secrets and configurations, and CPU-only workloads. Then advanced concepts: improve inference speed with vLLM, deploying Inferentia, loading model weights faster, and multi-GPU inference.

The categories after that are use-case shaped. Endpoints covers WebSockets and simple output streaming. Integrations covers a Langchain QA system and a virtual calendar assistant with Langsmith. Large Language Models covers an OpenAI compatible endpoint with vLLM, streaming results from Falcon 7B to a client, and building a code review tool with Winston.

Voice is the largest category by a wide margin, with ten entries: Whisper transcription, a realtime voice agent, a voice agent using current context with RAG, a WebSockets-based voice agent using Twilio, cloning voices with XTTS, building your own OpenAI realtime API replacement, an outbound LiveKit agent, a real-time voice AI agent, transcribing with FasterWhisper, and a Sesame Conversational Speech Model API.

Image and video has seven, from deploying ComfyUI through ControlNet logo detection, the SDXL refiner, SDXL Lightning, fast and regular stable diffusion, and generating images faster with SDXL. Then application demos: a sales training tool with Mistral, finding products for sale using a live video stream, an AI commentator and an AI coding agent.

The last three categories are the platform mechanics rather than the applications. Batching has four, with LitServe on CPU and on GPU, and batching with vLLM and with transformers. Python apps has two, a FastAPI deployment over ASGI and a Gradio interface. And there are directories in the repository that the README does not list at all, including 12-training, 13-misc, 13-utils and 14-embeddings.

That last point is a small reminder that the README's index is not exhaustive, and the directory listing is the more reliable source of what exists.

Reading the index as a feature list for the platform

There is a second reading of this catalogue, and it is more useful than the list of examples itself.

Look at what the categories assume. Batching exists as a first class topic with four examples, which means the platform expects people to run a model behind a queue rather than one request per instance. Multi-GPU inference has its own example, and so does deploying Inferentia, which is AWS silicon, which means the platform is not tied to a single accelerator vendor.

Loading model weights faster has its own entry, which tells you the platform recognises that cold start for a large model is a first-order problem rather than an afterthought.

Endpoints covers WebSockets and streaming, so long-lived connections and incremental output are supported rather than bolted on. The OpenAI compatible endpoint with vLLM tells you that the goal is to be a drop-in for code written against a different provider's API, which is the fastest way to evaluate a switch.

The CPU-only workloads example under getting started is the most informative single entry in the list. It says that not every workload needs a GPU, and that the platform is usable for the CPU-bound parts of a pipeline.

And the voice category, with ten entries, is a signal about where the demand is. Realtime agents, RAG over conversation history, telephony via Twilio, outbound calling through LiveKit, voice cloning, multilingual agents, and a comparison against a competitor's realtime API. That is a coherent product line rather than a set of scattered demos.

What each example folder is expected to contain

The contribution instructions specify a template, and the list of required contents tells you what a good example looks like.

A new example should include a clear README with a description of the example, requirements, step-by-step setup instructions, expected outputs, and optionally a link to a blog post or tutorial video.

That expected outputs entry is the one that matters most for an example repository. It means the reader should be able to tell whether their deployment is behaving correctly, which for an ML example is the difference between a running container and a working model.

The contribution guidelines then state the quality bar in six points: ensure your example is well-documented, make sure that your example deploys successfully, add appropriate error handling, follow the code style as much as possible, test your example thoroughly, and update the main README.md to include your example.

That last one is a nice touch for a catalogue repository. The index is not generated, so adding an example is a two-part change, the folder and the line in the README, and the second part is what keeps the list honest.

The process itself is a fork, a branch, the example, and then sharing the fork on the Discord community or on social media, with highly valuable examples for the community merged into the master repo. The maintainers also run a swag incentive, where each merged contribution gets swag, with bonus swag for a contribution that includes a blog post explaining the example or a tutorial video demonstrating it.

Where these examples are the wrong thing to copy

Three cases, and they are all about what a reference implementation is good for.

First, if you are not on Cerebrium. The whole repository is deployment recipes for one hosted platform, with its own CLI, its own configuration surface and its own model loading behaviour. There is nothing portable here except the ideas. If your target is Kubernetes with your own operator, or a different serverless GPU provider, the useful part is the model serving pattern in each folder, not the deployment instructions.

Second, if you want the production version of an application. These are demonstrations, and a demonstration trades away the things production needs. There is no mention of monitoring, cost control, authentication beyond the secrets example, or scaling beyond the multi-GPU inference entry. The billing model of a per-second GPU platform makes runaway costs a real risk that a demo does not model.

Third, if you are evaluating the model rather than the platform. Several examples are about how to serve a specific model efficiently, and the choices in them, which inference engine, which quantisation, which attention implementation, are tuned for a particular model on particular hardware. Copying one without measuring on your own workload is how you end up with a slower stack than the default.

There is a licensing point to settle as well. The repository records no named licence in its metadata, with the terms classified as unknown. For a directory of example code that you might copy into a commercial project, that is worth resolving before you lift a folder wholesale.

Support channels, and what the docs site is for

Three support routes are listed at the end of the README. The documentation is at docs.cerebrium.ai, the Discord community is at a discord.gg link, and there is a support email at [email protected].

The documentation link is the one that matters for understanding the platform itself, because this repository does not document the platform. It shows what can be built on it and how to deploy a given example, and it points at the docs site for what the platform does.

The distinction matters for anyone evaluating a hosted GPU service, because the questions you need answered before committing are about the platform, not the examples. What a cold start costs, how a GPU is charged when it is idle, what the concurrency limits are, how a persistent volume works, what happens when the model exceeds the instance memory, and how a queue behaves under load. Those are platform questions, and the repository answers none of them.

The examples do answer a different set of questions well. Whether a specific combination, this inference engine with this quantisation on this accelerator, produces the throughput you need. Whether a realtime voice agent is worth building with this platform's endpoint model. Whether the batching examples translate to your request pattern.

The repository is also honest about being a community project rather than a product surface. It has no releases, its licence is not recorded, and the index has grown by addition, with the numbering showing the order in which the platform's capabilities were added.

Editorial conclusion

Adopt this repository as a reference if you are deploying models on Cerebrium and want a working starting point for a specific pattern, since each folder is self-contained and deployable. Do not adopt it as a library, because there is nothing to install and no API to depend on. Verify first that the example closest to your use case still matches the platform's current behaviour, and read the README inside that example folder, since the top level list gives only titles and the deployment detail lives per project.

Frequently asked questions

Official sources

1. CerebriumAI/examples on GitHub
2. Issues
3. Project website
4. README