# Mistral 7B on GCP

> Mistral 7B on GCP - Learn how to integrate Mistral 7B on Google GCP and DataRobot.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-06T18:17:09.575601+00:00` (UTC).

## Primary page

- [Mistral 7B on GCP](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/custom-model-dev/mistral-7b.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page.
- [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html): Linked from this page.
- [Custom model development](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/custom-model-dev/index.html): Linked from this page.

## Documentation content

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/generative_ai/Mistral%207B%20on%20Google%20GCP/Mistral%207B%20on%20Google%20GCP.ipynb)

There are a wide variety of open source large language models (LLMs). For example, there has been a lot of interest in [Llama](https://llama.meta.com/) and variations such as Alpaca, Vicuna, Falcon, and Mistral. Because these LLMs require expensive GPUs, users often want to compare cloud providers to find the best hosting option. In this accelerator, you will work with Google Cloud Platform to host Llama 2.

You may also want to integrate with the cloud provider that hosts your Virtual Private Cloud (VPC) so that you can ensure proper authentication and access it only from within the VPC. While this accelerator uses authentication over the public internet, it is possible to leverage Google's cloud infrastructure to adjust and suit your cloud architectural needs, including provisioning scaleout policies.

Finally, by leveraging Vertex AI in a managed format, you can integrate that infrastructure into your existing stack to meet monitoring needs—things like monitoring service health, CPU usage, and low-level alerting  to billing, cost attribution, and account management and, using GCP's tools to route information into BigQuery for ad hoc analytics, log exploration, and more.

For information about Mistral, you can read the model card on [HuggingFace](https://huggingface.co/mistralai/Mistral-7B-v0.1), the [Arxiv page](https://arxiv.org/abs/2310.06825) and the [release announcement](https://mistral.ai/news/announcing-mistral-7b/). It is available under an [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).