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# Server Requirements

This page describes hardware specifications for hosting CoderFlow. Three tiers are provided as starting points; pick the one that matches your expected workload. All tiers assume a Linux distribution (Ubuntu Server LTS is recommended) with Docker as the container runtime.

## Configuration Tiers

| Component        | Light Workload         | Medium Workload    | Heavy Workload          |
| ---------------- | ---------------------- | ------------------ | ----------------------- |
| **CPU**          | 4–8 vCPUs              | 8–12 vCPUs         | 16+ vCPUs               |
| **RAM**          | 16 GB                  | 32 GB              | 64 GB+                  |
| **Storage Type** | SSD (SATA or NVMe)     | NVMe SSD           | NVMe SSD (RAID optional)|
| **Storage Size** | 256–512 GB             | 512 GB – 1 TB      | 1 TB+                   |
| **Network**      | 1 Gbps                 | 1–10 Gbps          | 10 Gbps+                |
| **OS**           | Linux                  | Linux              | Linux                   |

## Critical Requirements

### Storage

:::warning

SSD storage is required. Spinning disks (HDDs) create severe I/O bottlenecks for container image pulls, layer caching, and volume mounts, and will make the server feel slow even under light load.

:::

When possible, put Docker's data directory (`/var/lib/docker`) on a separate volume from the OS partition. This keeps container churn from competing with the OS for I/O and makes it easier to grow or replace storage independently.

### Memory

Reserve 2–4 GB of RAM for the host operating system and the container runtime itself; the remainder is available for running tasks.

Swap (sized roughly equal to physical RAM) can help absorb short bursts of memory pressure, but only enable it on SSD-backed storage — swapping to a spinning disk will tank performance.

### Network

Make sure the server has enough bandwidth to pull container images quickly, especially when building or refreshing environments.

If CoderFlow connects to external servers or services — for example, an IBM i system — host it on the same network and as physically close to those systems as possible. Round-trip latency between CoderFlow and the systems it works with has a noticeable impact on source import, build, and debug workflows.
