--- title: "Run InvokeAI on a cloud GPU from $0.037/hr | PowerGPU" description: "InvokeAI on a fixed-price cloud GPU: one-click template, running in ~30 s, from $0.037/hr on RTX 4070. Best GPUs, volumes, per-second billing." url: https://powergpu.ai/templates/invokeai last_modified: 2026-09-14T11:04:20+00:00 prices_as_of: 2026-09-14 site: PowerGPU (powergpu.ai) --- Template · Image generation · CUDA 12.9 # Run InvokeAI on a cloud GPU, in 30 seconds A polished Stable Diffusion studio — canvas, layers and workflow nodes. InvokeAI is the polished studio for image generation: an infinite canvas, layers, inpainting, workflow nodes and a model manager that understands SD 1.5, SDXL and Flux. It suits illustrators and teams who want a designed tool rather than a graph editor. From **$0.037** /hr on an interruptible RTX 4070. InvokeAI powergpu/invokeai (CUDA 12.9) (ARM) (SSH) (Jupyter) Pinned image, rebuilt on upstream releases. Override env, ports and disk at deploy. ### Running in ~30 s Pre-cached image, ports mapped, TLS terminated — the stack is working before you finish reading this. ### Template is free You pay the GPU price only: InvokeAI on a RTX 4070 is $0.075/hr on-demand, $0.037/hr interruptible, billed per second. ### Volumes for state Models, datasets and outputs live on a $0.08/GB/mo volume; the instance stays disposable. ### Private by default Dedicated GPU, encrypted disk, crypto payments, no KYC, no stored IPs — Jupyter and SSH behind your own credentials. ## Best GPUs for InvokeAI Three price points that run this template well — Good, Better, Best. Every model in the catalogue works; these are the value picks. | Tier | GPU | VRAM | On-demand | Interruptible | Why this card | | | --- | --- | --- | --- | --- | --- | --- | | (Good) | [RTX 4070](https://powergpu.ai/gpu/rtx-4070) | 12 GB | $0.075 | $0.037 | 12 GB runs SD 1.5 and SDXL with FP8 comfortably at low cost. | [Deploy](https://cloud.powergpu.ai/?gpu=rtx-4070) | | (Better) | [RTX 4090](https://powergpu.ai/gpu/rtx-4090) | 24 GB | $0.327 | $0.163 | 24 GB for Flux and large canvases. | [Deploy](https://cloud.powergpu.ai/?gpu=rtx-4090) | | (Best) | [RTX 5090](https://powergpu.ai/gpu/rtx-5090) | 32 GB | $0.439 | $0.219 | 32 GB for multi-model pipelines and big batch renders. | [Deploy](https://cloud.powergpu.ai/?gpu=rtx-5090) | Need more VRAM? The [full catalogue](https://powergpu.ai/gpus) lists all 80 models with live availability; the [VRAM guide](https://powergpu.ai/guides/llm-vram-requirements) sizes models to cards. ## Deploy InvokeAI from the console, CLI or API Pick the template in the [console](https://cloud.powergpu.ai/) deploy bar, or script it: - **Console** — filter by GPU, choose *InvokeAI* in the template picker, set disk and env, deploy. - **CLI** — pip install powergpu, then the command on the right. [CLI reference](https://powergpu.ai/cli). - **API** — POST /v1/instances with "template": "invokeai". [REST reference](https://powergpu.ai/api). - **Own image** — any OCI reference works too; we inject the NVIDIA runtime. [Template docs](https://powergpu.ai/docs/templates). *deploy — invokeai* ``` $ powergpu launch --gpu rtx-4070 --template invokeai \ --disk 100 --volume models:/workspace/models ✓ instance i-7a41c0e2 running (27.9s) # InvokeAI · RTX 4070 · $0.075/hr · per second # https://i-7a41c0e2.powergpu.ai:8888 (TLS) $ powergpu stop i-7a41c0e2 # billing ends this second ``` ## What is inside the InvokeAI template - **Image**: powergpu/invokeai - **CUDA**: CUDA 12.9 - **Access**: also builds for ARM hosts · SSH shell · JupyterLab on a mapped port - **Category**: [Image generation](https://powergpu.ai/templates#image) - **Storage**: Instance NVMe disk (sized at deploy) + optional [network volumes](https://powergpu.ai/products/volumes) - **Billing**: GPU price only, per second — no template fee, no setup fee ## Other image generation templates - [ComfyUI on a cloud GPU](https://powergpu.ai/templates/comfyui) — Node-based image and video workflows with ComfyUI-Manager pre-installed. - [SD WebUI Forge on a cloud GPU](https://powergpu.ai/templates/sd-webui-forge) — The Forge fork of the SD WebUI — faster attention, lower VRAM, same extensions. - [SD WebUI A1111 on a cloud GPU](https://powergpu.ai/templates/sd-webui-a1111) — The classic AUTOMATIC1111 Stable Diffusion WebUI with the full extension ecosystem. - [Fooocus on a cloud GPU](https://powergpu.ai/templates/fooocus) — Prompt-and-go image generation — Midjourney-style simplicity on SDXL. - [SwarmUI on a cloud GPU](https://powergpu.ai/templates/swarmui) — A ComfyUI-backed UI that scales generation across multiple GPUs. - [All 37 templates](https://powergpu.ai/templates) ## InvokeAI on a cloud GPU: FAQ Environment variables, ports and custom images are covered in the [template docs](https://powergpu.ai/docs/templates). **How much does it cost to run InvokeAI on a cloud GPU?** Only the GPU price — the template is free. From $0.037/hr on an interruptible RTX 4070, $0.075/hr on-demand on a RTX 4070. Billing is per second, so an hour of tinkering costs an hour, not a day. Storage is $0.08/GB/month. **How long does InvokeAI take to start?** About 30 seconds from the deploy click: the image is pre-cached on hosts, ports are mapped and TLS is terminated for you. Restarting a stopped instance is faster, and your disk is exactly as you left it. **Can I keep my models and outputs between sessions?** Yes — attach a volume at deploy. Everything on the volume survives instance destruction and mounts on the next instance in the region in seconds, at $0.08/GB/month. The instance disk itself survives stop/start but not destroy. **Can I import my Automatic1111 models?** Yes — point the model manager at a mounted volume containing your.safetensors files; InvokeAI scans and registers them. --- *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/invokeai · Site index for AI assistants: https://powergpu.ai/llms.txt · Full content: https://powergpu.ai/llms-full.txt