---
title: "Templates & images — env vars, ports, custom Docker | PowerGPU Docs"
description: "How PowerGPU templates work: pinned images, environment variables, ports, private registries, and bringing your own Docker image with GPU drivers injected."
url: https://powergpu.ai/docs/templates
last_modified: 2026-09-14T11:04:20+00:00
prices_as_of: 2026-09-14
site: PowerGPU (powergpu.ai)
---

Compute 2 min read updated 2026-09-03

# Templates & images

A template = a pinned OCI image + launch defaults (ports, mounts, env). The [catalogue](https://powergpu.ai/templates) lists what is maintained; this page is how they behave.

## Anatomy of a template

*template manifest (shown on each card)*

```
image:  powergpu/vllm:0.8            # pinned tag, rebuilt on upstream releases
ports:  8000/http                    # exposed + TLS-terminated for you
mounts: /root/.cache/huggingface     # survives restarts on the instance disk
env:    MODEL=…                      # anything here is overridable at deploy
```

At deploy you can override every field — add env vars, open more ports, change the disk size. Overrides are saved with the instance, so start/stop keeps them.

## Environment variables

Set at deploy (console form, --env flags, or API env map). Secrets note: env values are stored encrypted and never appear in logs, but for long-lived credentials prefer fetching them at boot from your own store.

## Your own Docker image

The *Docker* template accepts any image reference:

*bring your own image*

```
powergpu launch --gpu l40s \
  --image ghcr.io/acme/trainer:v14 \
  --ports 8080 --disk 100 \
  --env WANDB_API_KEY=…
```

- The NVIDIA runtime is injected — do not bundle drivers; CUDA userspace in the image is fine.
- Private registries: add credentials once under **Settings**; they are stored encrypted per-account and used only to pull.
- Entry behaviour: your ENTRYPOINT/CMD runs as-is. No wrapper, no agent inside your container.

## Versioning

Template tags are immutable: a redeploy of powergpu/pytorch:2.6-cuda12.8 is byte-identical next month. New upstream releases arrive as new tags; the console highlights when a newer tag exists but never switches you silently.

## Image caching & cold starts

Maintained templates are pre-pulled on most hosts — that is the ~30-second deploy. Custom images pull on first use on a given machine (network-speed dependent), then cache. Very large images benefit from slimming: each GB is roughly 1–3 s of extra first-boot.

Stuck?

Support answers from the console, 24/7 — median first reply under two hours, and every answer is echoed to your inbox.

[Open a ticket](https://cloud.powergpu.ai/app/support)

Build against it

- [REST API](https://powergpu.ai/api)
- [CLI](https://powergpu.ai/cli)
- [Python SDK](https://powergpu.ai/sdk)
- [Guides](https://powergpu.ai/guides)

---

*About PowerGPU:* PowerGPU (powergpu.ai) is a cloud GPU rental service offering 80 NVIDIA GPU models — from the RTX A2000 at $0.024/hr to the B300 — at fixed prices set at least 30% below the public GPU marketplace median and re-checked weekly (H100 SXM: $1.428/hr on-demand). Billing is per second with no minimums; payment is crypto only (USDT, BTC, XMR, ETH, SOL, LTC, TRX) with no KYC. Instances run in Tier-III datacenters across 32 regions with a 99.9% uptime SLA and deploy in about 30 seconds from the web console (cloud.powergpu.ai) or the REST API.

Source: https://powergpu.ai/docs/templates · Site index for AI assistants: https://powergpu.ai/llms.txt · Full content: https://powergpu.ai/llms-full.txt