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

# Resourcing

> Recommended CPU / RAM / Disk to run Hymalaia

## Running Locally

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

## Single Cloud Instance

For small- to mid-scale deployments, setting everything up on a **single instance** (e.g., AWS EC2, GCP Compute Engine, Azure VM) via **Docker Compose** is the simplest way to get started.\
Check out our [EC2 deployment guide](https://docs.hymalaia.app/deployment/ec2) for a step-by-step walkthrough.

We recommend the following for a single instance:

* **CPU**: ≥ 4 vCPU cores (≥ 8 vCPU cores recommended for larger document volumes)
* **Memory**: ≥ 16 GB RAM
* **Disk**: ≥ 50 GB + \~2.5× the size of the indexed documents

> 💡 **Note:** Vespa does **not allow writes** when disk usage exceeds 75%, so always allocate extra disk space.

> 🧹 **Tip:** Old/unused Docker images can consume significant space. Run `docker system prune --all` regularly to free space.

> ⚠️ **Compatibility:** Vespa requires **Haswell (2013) or later CPUs**. For older CPUs, use the `vespaengine/vespa-generic-intel-x86_64` image. This version is slower but compatible.

## Kubernetes / AWS ECS

For deployments where each component gets its own resources, allocate at least:

| Component                | CPU  | Memory |
| ------------------------ | ---- | ------ |
| `api_server`             | 1    | 2Gi    |
| `background`             | 2    | 8Gi    |
| `indexing_model_server`  | 2    | 4Gi    |
| `inference_model_server` | 2    | 4Gi    |
| `postgres`               | 2    | 2Gi    |
| `vespa`                  | ≥4   | ≥8Gi   |
| `nginx`                  | 250m | 128Mi  |

> ⚠️ **Important:** The `vespa` component’s needs scale with the number of indexed documents.\
> For production, we **recommend more than the minimum** (e.g., 10 CPU, 20Gi+ RAM for 50GB of documents).

**Minimum Node Size:** 14 vCPU / 23GB RAM

## How Resource Requirements Scale

The primary factor for scaling is the **size of the indexed documents**.

For every **1GB of raw text documents**, expect:

* **+3GB RAM**
* **+0.5 CPU core**

Baseline (without document indexing):\
**4 vCPU** and **16GB RAM**

> 📘 A “1GB document” means raw text. PDFs with images may be large in file size, but the actual text may be small.

> 🧠 Dimensionality of embedding models and techniques like quantization can reduce requirements.

## Example: 10GB of Text Documents

### Index Component Requirements:

* **CPU**: 4 + 10 × 0.5 = **9 cores**
* **Memory**: 16 + 10 × 3 = **46GB**

### Single Instance Total Recommendation:

* **CPU**: ≥ 13 cores
* **Memory**: ≥ 50GB

> 🖥️ Suggested instance types: `m7g.4xlarge`, `c5.9xlarge`

### Kubernetes / AWS ECS Breakdown:

| Component                | CPU | Memory |
| ------------------------ | --- | ------ |
| `api_server`             | 1   | 2Gi    |
| `background`             | 2   | 8Gi    |
| `indexing_model_server`  | 2   | 4Gi    |
| `inference_model_server` | 2   | 4Gi    |
| `postgres`               | 2   | 4Gi    |
| `vespa`                  | 10  | 34Gi   |

**Total Recommended Node Size**: **17 CPU / 66Gi RAM**