# Requirements and installation choices

Choose a deployment profile and collect the infrastructure, credentials and model inputs it needs.

## Choose a profile

| Profile | You supply | Guide |
| --- | --- | --- |
| Existing AI endpoints | Reachable transcription/chat API roots, credentials and accepted model IDs | [Install with existing endpoints](/install/external-ai.md) |
| Self-hosted AI | NVIDIA host, model access/cache and sufficient compute for Whisper and GPT-OSS | [Install self-hosted AI](/install/self-hosted-ai.md) |

Both recipes include the API, jobs, one-shot migrations, PostgreSQL, RustFS,
bucket initialization, management app and an HTTPS proxy. A client is installed
or hosted separately. Product images require registry pull access; product
source access is unnecessary.

## Core infrastructure

- Linux host with Docker Engine and Compose v2 supporting GPU reservations for
  the GPU profile. Docker Desktop can preview the docs, but is not the reference
  GPU deployment target.
- DNS hostname, HTTPS certificate strategy and network access from client devices.
  The included proxy needs ports 80/443 free and inbound 443 for its ACME TLS challenge.
- Persistent storage for PostgreSQL, recordings, upload drafts, certificates and
  caches; capacity and retention depend on your workload.
- GHCR credentials authorized for the private Dicto images and, when selected,
  `hippolabs-org/llm-server`. Accept model terms and obtain separate model access
  credentials where required.
- New credentials for the database, auth secret, storage and bootstrap account.
  Decide who will back up and recover the installation before rollout.

## GPU profile

Install NVIDIA drivers and Container Toolkit compatible with your pinned inference
images. Check `nvidia-smi` on the host and GPU visibility inside an approved CUDA
container before troubleshooting model serving. The bundle assigns devices 0/1
to Whisper and device 2 to GPT-OSS, preserving the selected maintained allocations.
It therefore needs those devices or an explicit allocation edit.

The maintained host has 96 GB-class GPUs and additional workloads. Those
allocations are not universal minimum requirements. Model weights, context size,
parallelism, KV cache and concurrency determine capacity. Start from the
[model guides](/components/llm.md), check model access and available VRAM/RAM/cache
disk, and validate on your own hardware. Model weights are downloaded separately
and are not inside the setup archive.

## Versions and validation

The [reference snapshot](/reference/versions.md) records source provenance and exact
product image digests. It also states what has been checked and which GPU/end-to-end
checks need an operator run. A valid Compose file is not a completed installation.