# System requirements

The short answer: **any [Apple Silicon Mac](https://www.amazon.com/stores/page/A29243E5-37F0-4858-ADDB-40E17FD781B7) running macOS 13 or
later, with enough free disk for your retention plan**.

## macOS versions

| Version | Status |
| --- | --- |
| macOS 13 Ventura | ✅ minimum |
| macOS 14 Sonoma | ✅ |
| macOS 15 Sequoia | ✅ |
| macOS 26 (current) | ✅ |
| macOS 12 Monterey or older | ❌ |

We track current macOS — the newest dot-release of the current major
is the canonical platform we test on.

## Memory

| RAM | Usable for |
| --- | --- |
| 8 GB | 8+ cameras / 10 fps detection |
| 16 GB | Comfortable for 8–16 cameras, easily + other apps |
| 24+ GB | More cameras, GenAI descriptions, semantic search, run other memory-hungry apps |

Each ffmpeg decoder is the dominant memory consumer; budget 100–
250 MB per camera for ffmpeg + the detector pipeline. Fregata
itself (the Python core, the Swift app, nginx, go2rtc) totals about
1 GB of RAM.

## Disk

The detector model and the app are around 2 GB combined.
**Recordings dominate** the disk usage:

| Cameras | Per day @ motion-only | 14-day disk budget |
| --- | --- | --- |
| 1 | 4–10 GB | 60–140 GB |
| 4 | 16–40 GB | 250–560 GB |
| 8 | 32–80 GB | 500 GB – 1.2 TB |
| 16 | 64–160 GB | 1–2 TB |

External Thunderbolt SSDs work fine; external USB SSDs
work if they're 3.0 or better. A networked NAS is also a fine choice.

See [Recordings & retention](/guides/recordings-and-retention/) for
how to dial these numbers up or down.

## Network

- **Cameras on Ethernet** if at all possible. RTSP over Wi-Fi works
  but is sensitive to packet loss; a noisy Wi-Fi → Mac path
  manifests as detection drop-outs and choppy recordings.
- **Mac on Ethernet** for 4+ cameras. Multi-stream RTSP plus the
  HA integration's MJPEG fan-out can saturate Wi-Fi quickly.

## What we don't run on

Listed for completeness so you don't waste time:

- **Intel Macs** — no ANE.
- **iPads / iPhones** — wrong app shape; we'd have to rewrite for
  iOS lifecycle and we're not going to.
- **Linux / Windows** — that's
  [Frigate](https://docs.frigate.video/), the project we're built
  on. Use it directly there, it's excellent!
- **Docker on macOS** — possible, but nullifies every reason to use
  Fregata over Frigate. The hardware acceleration paths don't
  cross the Hypervisor.framework boundary cleanly.
- **A virtualized macOS guest** — same problem; ANE access from a
  guest VM is impossible. Fregata will run, but you lose most of the benefits.

:::note
If your situation isn't on the supported list and you'd like it
to be, [file a discussion](https://github.com/3rdBitLabs/Fregata/discussions).
Some scenarios (specific cloud-Mac providers, headless boot
flows) are easier to support than others.
:::