---
title: "GPU Templates: ComfyUI, vLLM, PyTorch, Ollama & 33 More | PowerGPU"
description: "37 one-click GPU templates with official images: PyTorch, vLLM, Ollama, ComfyUI, Axolotl, full VMs — or any Docker image. Running in ~30 s on fixed-price GPUs."
url: https://powergpu.ai/templates
last_modified: 2026-09-14T11:04:20+00:00
prices_as_of: 2026-09-14
site: PowerGPU (powergpu.ai)
---

Templates · 37 maintained stacks

# 37 GPU templates: from "deploy" to working stack in 30 seconds

Every template is a pinned, GPU-ready image with ports mapped and storage mounted — official builds of the tools you already use. Launch PyTorch, serve with vLLM, generate with ComfyUI, fine-tune with Axolotl — or point us at your own Docker image.

## Base & frameworks (6)

- **[NVIDIA CUDA](https://powergpu.ai/templates/nvidia-cuda)** The bare CUDA base image — drivers, toolkit, SSH and Jupyter. Build your own stack on top. (CUDA 13) (ARM) (SSH) (Jupyter) powergpu/base:cuda13 [GPUs & details](https://powergpu.ai/templates/nvidia-cuda) [Deploy](https://cloud.powergpu.ai/)
- **[PyTorch](https://powergpu.ai/templates/pytorch)** The training and research default: PyTorch 2.6, cuDNN, JupyterLab and SSH out of the box. (CUDA 12.8) (ARM) (SSH) (Jupyter) powergpu/pytorch:2.6-cuda12.8 [GPUs & details](https://powergpu.ai/templates/pytorch) [Deploy](https://cloud.powergpu.ai/)
- **[TensorFlow CUDA](https://powergpu.ai/templates/tensorflow-cuda)** TensorFlow with GPU support, Keras and TensorBoard on a mapped port. (CUDA 12.1) (SSH) (Jupyter) powergpu/tensorflow:cuda12.1 [GPUs & details](https://powergpu.ai/templates/tensorflow-cuda) [Deploy](https://cloud.powergpu.ai/)
- **[PyTorch NGC](https://powergpu.ai/templates/pytorch-ngc)** NVIDIA's NGC-optimised PyTorch build — tuned kernels for the datacenter cards. (SSH) (VM) nvcr.io/nvidia/pytorch [GPUs & details](https://powergpu.ai/templates/pytorch-ngc) [Deploy](https://cloud.powergpu.ai/)
- **[NVIDIA RAPIDS](https://powergpu.ai/templates/nvidia-rapids)** GPU-accelerated data science — cuDF, cuML, cuGraph in a notebook. (CUDA 13) (ARM) (SSH) (Jupyter) rapidsai/notebooks [GPUs & details](https://powergpu.ai/templates/nvidia-rapids) [Deploy](https://cloud.powergpu.ai/)
- **[All-in-One App Studio](https://powergpu.ai/templates/all-in-one-app-studio)** A launcher bundling the most-used AI apps behind one desktop — pick and run. (CUDA 12.9) (SSH) (Jupyter) powergpu/aio-studio [GPUs & details](https://powergpu.ai/templates/all-in-one-app-studio) [Deploy](https://cloud.powergpu.ai/)

## LLM serving & chat (9)

- **[vLLM](https://powergpu.ai/templates/vllm)** Production LLM serving with PagedAttention — an OpenAI-compatible endpoint from any HF model. (CUDA 13) (ARM) (SSH) (Jupyter) powergpu/vllm [GPUs & details](https://powergpu.ai/templates/vllm) [Deploy](https://cloud.powergpu.ai/)
- **[vLLM Omni](https://powergpu.ai/templates/vllm-omni)** vLLM extended for multimodal models — vision and audio inputs on the same fast server. (CUDA 12.9) (SSH) (Jupyter) powergpu/vllm-omni [GPUs & details](https://powergpu.ai/templates/vllm-omni) [Deploy](https://cloud.powergpu.ai/)
- **[SGLang](https://powergpu.ai/templates/sglang)** High-throughput serving with RadixAttention — excels at structured and agentic workloads. (CUDA 13) (ARM) (SSH) (Jupyter) powergpu/sglang [GPUs & details](https://powergpu.ai/templates/sglang) [Deploy](https://cloud.powergpu.ai/)
- **[Llama.cpp](https://powergpu.ai/templates/llama-cpp)** GGUF inference with a built-in server — the lightest way to run quantized models. (CUDA 12.9) (ARM) (SSH) (Jupyter) powergpu/llama-cpp [GPUs & details](https://powergpu.ai/templates/llama-cpp) [Deploy](https://cloud.powergpu.ai/)
- **[Ollama](https://powergpu.ai/templates/ollama)** Pull and run quantized models in one command, with a clean REST API. (ARM) (SSH) (Jupyter) powergpu/ollama [GPUs & details](https://powergpu.ai/templates/ollama) [Deploy](https://cloud.powergpu.ai/)
- **[Open WebUI (Ollama)](https://powergpu.ai/templates/open-webui-ollama)** A full chat UI over Ollama — conversations, RAG and model management in the browser. (CUDA 12.9) (SSH) (Jupyter) powergpu/openwebui [GPUs & details](https://powergpu.ai/templates/open-webui-ollama) [Deploy](https://cloud.powergpu.ai/)
- **[Oobabooga Text Gen](https://powergpu.ai/templates/oobabooga-text-gen)** The text-generation WebUI & API — load GPTQ, EXL2 or GGUF with extensions. (CUDA 12.4) (SSH) (Jupyter) powergpu/oobabooga [GPUs & details](https://powergpu.ai/templates/oobabooga-text-gen) [Deploy](https://cloud.powergpu.ai/)
- **[HuggingFace TGI](https://powergpu.ai/templates/huggingface-tgi)** Hugging Face Text Generation Inference — battle-tested production serving with an API. (SSH) (Jupyter) ghcr.io/huggingface/text-generation-inference [GPUs & details](https://powergpu.ai/templates/huggingface-tgi) [Deploy](https://cloud.powergpu.ai/)
- **[Langflow (Ollama)](https://powergpu.ai/templates/langflow-ollama)** Visually build LLM pipelines and agents on top of a local Ollama backend. (CUDA 12.6) (SSH) (Jupyter) powergpu/langflow [GPUs & details](https://powergpu.ai/templates/langflow-ollama) [Deploy](https://cloud.powergpu.ai/)

## Training & fine-tuning (5)

- **[Axolotl — Fine Tuning](https://powergpu.ai/templates/axolotl-fine-tuning)** Fine-tune Llama, Qwen or Mistral from one YAML — LoRA, QLoRA and multi-GPU FSDP. (CUDA 12.6) (ARM) (SSH) (Jupyter) axolotlai/axolotl-cloud [GPUs & details](https://powergpu.ai/templates/axolotl-fine-tuning) [Deploy](https://cloud.powergpu.ai/)
- **[Unsloth Studio](https://powergpu.ai/templates/unsloth-studio)** 2× faster, lower-VRAM fine-tuning — Unsloth kernels with a notebook workflow. (CUDA 12.9) (SSH) (Jupyter) powergpu/unsloth-studio [GPUs & details](https://powergpu.ai/templates/unsloth-studio) [Deploy](https://cloud.powergpu.ai/)
- **[Kohya's GUI](https://powergpu.ai/templates/kohya-s-gui)** Train SDXL and Flux image LoRAs in a browser GUI — dataset in,.safetensors out. (CUDA 12.6) (SSH) (Jupyter) powergpu/kohyas-gui [GPUs & details](https://powergpu.ai/templates/kohya-s-gui) [Deploy](https://cloud.powergpu.ai/)
- **[Flux Gym](https://powergpu.ai/templates/flux-gym)** A dead-simple UI for training Flux LoRAs, built on the Kohya scripts. (ARM) (SSH) (Jupyter) powergpu/fluxgym [GPUs & details](https://powergpu.ai/templates/flux-gym) [Deploy](https://cloud.powergpu.ai/)
- **[Ostris AI Toolkit](https://powergpu.ai/templates/ostris-ai-toolkit)** The AI-Toolkit trainer for Flux and diffusion models, with a web UI. (CUDA 12.9) (SSH) (Jupyter) powergpu/ostris-ai-toolkit [GPUs & details](https://powergpu.ai/templates/ostris-ai-toolkit) [Deploy](https://cloud.powergpu.ai/)

## Image generation (6)

- **[ComfyUI](https://powergpu.ai/templates/comfyui)** Node-based image and video workflows with ComfyUI-Manager pre-installed. (CUDA 13) (ARM) (SSH) (Jupyter) powergpu/comfyui [GPUs & details](https://powergpu.ai/templates/comfyui) [Deploy](https://cloud.powergpu.ai/)
- **[InvokeAI](https://powergpu.ai/templates/invokeai)** A polished Stable Diffusion studio — canvas, layers and workflow nodes. (CUDA 12.9) (ARM) (SSH) (Jupyter) powergpu/invokeai [GPUs & details](https://powergpu.ai/templates/invokeai) [Deploy](https://cloud.powergpu.ai/)
- **[SD WebUI Forge](https://powergpu.ai/templates/sd-webui-forge)** The Forge fork of the SD WebUI — faster attention, lower VRAM, same extensions. (CUDA 12.8) (ARM) (SSH) (Jupyter) powergpu/sd-forge [GPUs & details](https://powergpu.ai/templates/sd-webui-forge) [Deploy](https://cloud.powergpu.ai/)
- **[SD WebUI A1111](https://powergpu.ai/templates/sd-webui-a1111)** The classic AUTOMATIC1111 Stable Diffusion WebUI with the full extension ecosystem. (CUDA 12.1) (ARM) (SSH) (Jupyter) powergpu/a1111 [GPUs & details](https://powergpu.ai/templates/sd-webui-a1111) [Deploy](https://cloud.powergpu.ai/)
- **[Fooocus](https://powergpu.ai/templates/fooocus)** Prompt-and-go image generation — Midjourney-style simplicity on SDXL. (CUDA 12.1) (SSH) (Jupyter) powergpu/fooocus [GPUs & details](https://powergpu.ai/templates/fooocus) [Deploy](https://cloud.powergpu.ai/)
- **[SwarmUI](https://powergpu.ai/templates/swarmui)** A ComfyUI-backed UI that scales generation across multiple GPUs. (CUDA 12.4) (SSH) (Jupyter) powergpu/swarmui [GPUs & details](https://powergpu.ai/templates/swarmui) [Deploy](https://cloud.powergpu.ai/)

## Video generation (2)

- **[Wan2GP](https://powergpu.ai/templates/wan2gp)** Run Wan and other video-generation models on modest VRAM, with a simple UI. (CUDA 12.9) (SSH) (Jupyter) powergpu/wan2gp [GPUs & details](https://powergpu.ai/templates/wan2gp) [Deploy](https://cloud.powergpu.ai/)
- **[Open-Sora](https://powergpu.ai/templates/open-sora)** The open text-to-video model, ready to generate and fine-tune. (CUDA 12.1) (SSH) (Jupyter) powergpu/open-sora [GPUs & details](https://powergpu.ai/templates/open-sora) [Deploy](https://cloud.powergpu.ai/)

## Audio & speech (3)

- **[Whisper WebUI & API](https://powergpu.ai/templates/whisper-webui-api)** Batch-transcribe audio with faster-whisper behind a UI and a REST endpoint. (CUDA 12.6) (ARM) (SSH) (Jupyter) powergpu/whisper [GPUs & details](https://powergpu.ai/templates/whisper-webui-api) [Deploy](https://cloud.powergpu.ai/)
- **[Voicebox TTS](https://powergpu.ai/templates/voicebox-tts)** Text-to-speech and voice cloning with a web interface and API. (CUDA 12.1) (SSH) (Jupyter) powergpu/voicebox [GPUs & details](https://powergpu.ai/templates/voicebox-tts) [Deploy](https://cloud.powergpu.ai/)
- **[ACE Step 1.5](https://powergpu.ai/templates/ace-step-1-5)** The ACE-Step music generation model with a UI and API. (CUDA 12.4) (SSH) (Jupyter) powergpu/acestep [GPUs & details](https://powergpu.ai/templates/ace-step-1-5) [Deploy](https://cloud.powergpu.ai/)

## Desktops & VMs (4)

- **[Linux Desktop](https://powergpu.ai/templates/linux-desktop)** A full GPU Linux desktop over the browser — XFCE, VNC/RDP, run GUI apps. (CUDA 12.1) (ARM) (SSH) (Jupyter) powergpu/linux-desktop [GPUs & details](https://powergpu.ai/templates/linux-desktop) [Deploy](https://cloud.powergpu.ai/)
- **[Pinokio](https://powergpu.ai/templates/pinokio)** The Pinokio 1-click app browser on a desktop — install AI apps with a click. (CUDA 13) (ARM) (SSH) (Jupyter) powergpu/pinokio [GPUs & details](https://powergpu.ai/templates/pinokio) [Deploy](https://cloud.powergpu.ai/)
- **[Ubuntu 22.04 VM](https://powergpu.ai/templates/ubuntu-22-04-vm)** A full KVM virtual machine — your own kernel, root, systemd, any stack. (SSH) (VM) powergpu/kvm:ubuntu-22.04 [GPUs & details](https://powergpu.ai/templates/ubuntu-22-04-vm) [Deploy](https://cloud.powergpu.ai/)
- **[Ubuntu Desktop VM](https://powergpu.ai/templates/ubuntu-desktop-vm)** The Ubuntu VM with a GNOME desktop over VNC/RDP — a full graphical workstation. (SSH) (VM) powergpu/kvm:ubuntu-desktop [GPUs & details](https://powergpu.ai/templates/ubuntu-desktop-vm) [Deploy](https://cloud.powergpu.ai/)

## Specialised (2)

- **[Unreal Pixel Streaming](https://powergpu.ai/templates/unreal-pixel-streaming)** Stream an Unreal Engine app to the browser — pixel streaming on a cloud GPU. (CUDA 12.8) (SSH) (Jupyter) powergpu/unreal-pixel-streaming [GPUs & details](https://powergpu.ai/templates/unreal-pixel-streaming) [Deploy](https://cloud.powergpu.ai/)
- **[Hashcat CUDA](https://powergpu.ai/templates/hashcat-cuda)** GPU password recovery for authorised security testing — CUDA-accelerated Hashcat. (SSH) dizcza/docker-hashcat [GPUs & details](https://powergpu.ai/templates/hashcat-cuda) [Deploy](https://cloud.powergpu.ai/)

## Bring your own image

Nothing here fits? The deploy form takes any OCI reference — we inject the NVIDIA runtime and run your entrypoint untouched.

- Public or private registries (credentials stored encrypted).
- Your ports, env vars and volume mounts, set at deploy.
- Pinned by tag — a redeploy is byte-identical next month.

*deploy — any image*

```
$ powergpu launch --gpu l40s \
    --image ghcr.io/acme/trainer:v14 \
    --ports 8080 --disk 100
✓ instance i-4fd02b11 running (31.2s)
```

## GPU templates FAQ

Environment variables, ports and custom images in the [template docs](https://powergpu.ai/docs/templates).

**What exactly is a template?**

A maintained container image plus sane launch defaults: exposed ports, volume mount points, health checks and environment variables. Pick one at deploy, override anything, and the instance boots straight into a working stack — usually in about 30 seconds.

**Can I use my own Docker image instead?**

Yes — the deploy form accepts any public or private OCI image reference (registry credentials are stored encrypted per-account). We inject the NVIDIA runtime; your entrypoint is untouched.

**What do the ARM, SSH, Jupyter and VM tags mean?**

They tell you how a template runs: ARM = also builds for ARM hosts, SSH = direct shell access, Jupyter = a notebook server on a mapped port, VM = a full virtual machine rather than a container.

**Do templates cost extra?**

No. You pay the GPU price — a ComfyUI session on an RTX 4090 is $0.327/hr, template included, billed per second.

---

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