> ## Documentation Index
> Fetch the complete documentation index at: https://docs.levelrail.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Self-host Text Embeddings Inference with Docker

> A lean server that turns text into embeddings with popular open models, on CPU with no extra setup.

A lean server that turns text into embeddings with popular open models, on CPU with no extra setup.

This page describes the Levelrail template for Text Embeddings Inference (AI). It deploys the services below as one Docker Compose app on your own server. Project site: [https://huggingface.co/docs/text-embeddings-inference](https://huggingface.co/docs/text-embeddings-inference).

Recommended memory: about 2048 MiB.

## Services, ports and volumes

| Service | Image | Container ports | Volumes |
| - | - | - | - |
| `tei` | `ghcr.io/huggingface/text-embeddings-inference:cpu-1.8` | 80 | `tei_data -> /data` |

## Environment variables

This template sets no environment variables.

Passwords and keys marked as generated are created for you when the app is deployed and stored as secrets. Values are not shown here.

## Deploy Text Embeddings Inference with Levelrail

In the dashboard, open **Apps**, choose **New app**, then **Browse templates**, and select Text Embeddings Inference. Review the Compose body and deploy.

With the CLI:

```bash theme={"dark"}
levelrail-cli templates deploy text-embeddings-inference --name my-text-embeddings-inference
```

See the [service template catalog](/templates-and-registry) for how templates work, and [Getting started](/getting-started) if you have not installed Levelrail yet.

## More AI templates

* [AnythingLLM](/self-host/anythingllm)
* [ComfyUI](/self-host/comfyui)
* [Flowise](/self-host/flowise)
* [Flowise (Postgres + Qdrant)](/self-host/flowise-postgres)
* [Jupyter GPU Notebook](/self-host/jupyter-gpu)

All templates are listed in the [self-host gallery](/self-host).


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