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

# Resourcing

> Recommended CPU / RAM / Disk to run Danswer

## Running Locally

When running locally through Docker, we recommend making at least 4vCPU cores and 10GB of RAM available to Docker
(16GB is preferred). This can be controlled in the **Resources** section of the Docker Desktop settings menu.

## Single Cloud Instance

We generally recommend setting everything up on a single instance
(e.g. an AWS EC2 instance, a Google Compute Engine instance, an Azure VM, etc.) via Docker Compose as it's
the simplest way to get started. For a step-by-step guide on how to do this, checkout our
[EC2 deployment guide](https://docs.danswer.dev/production/aws/ec2).

For most use cases a single reasonably sized instance should be more than enough to guarantee excellent
performance. A single instance should be able to effectively serve a small-medium sized organization
without issue.

If you go with this approach, we recommend:

* **CPU:** >= 4 vCPU cores (we recommend 8 vCPU cores if possible)
* **Memory:** >= 10 GB of RAM (for best performance, 16 is recommended)
  * Note: If you are switching embedding models, you will need >= 11 GB RAM as both sets of models need
    to be loaded in simultaneously
* **Disk:** >= 50 GB + \~2.5x the size of the indexed documents. Disk is generally very cheap, so
  we would recommend getting extra disk beyond this recommendation to be safe.
  * Note: Vespa does not allow writes when disk usage is >75%, so make sure to always have some headroom here
  * Note: old, unused docker images often take up a bunch of space when performing frequent upgrades of Danswer.
    To clean up these unused images, run: `docker system prune --all`.

For reference, we have chosen to give each Danswer Cloud customer a `m7g.xlarge` instance by default,
which has 4vCPU cores + 16GB of RAM + 200GB of disk space. We're comfortable using 4vCPU cores in a
production setting since we have dedicated GPU instances that run the embedding / cross-encoder models.
If you do not plan on setting that up, we would recommend going with 8vCPU cores (if possible) for a
production deployment.

## Kubernetes / AWS ECS

If you prefer to give each component it's own dedicated resources for more efficient scaling, we recommend giving each container at least the following resources:

`api_server` - 2500m CPU, 5Gi Memory

`background` - 2500m CPU, 5Gi Memory

`postgres` - 500m CPU, 1Gi Memory

`vespa` - >=2000m CPU, >= 4Gi Memory (may need to increase these depending on the number of documents you have indexed)

`nginx` - 250m CPU, 128Mi Memory