--- title: "Run an all-in-one AI app studio on a cloud GPU | PowerGPU" description: "All-in-One App Studio on a fixed-price cloud GPU: one-click template, running in ~30 s, from $0.163/hr on RTX 4090. Best GPUs, volumes, per-second billing." url: https://powergpu.ai/templates/all-in-one-app-studio last_modified: 2026-09-14T11:04:20+00:00 prices_as_of: 2026-09-14 site: PowerGPU (powergpu.ai) --- Template · Base & frameworks · CUDA 12.9 # Run an all-in-one AI app studio on a cloud GPU, in 30 seconds A launcher bundling the most-used AI apps behind one desktop — pick and run. One desktop, many launchers: the App Studio bundles the most-used community AI apps behind a single web UI so you can start ComfyUI, an LLM chat, a TTS tool or a trainer without building an image for each. Perfect for exploring tools before committing a workflow. From **$0.163** /hr on an interruptible RTX 4090. All-in-One App Studio powergpu/aio-studio (CUDA 12.9) (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: All-in-One App Studio on a RTX 4090 is $0.327/hr on-demand, $0.163/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 All-in-One App Studio 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 4090](https://powergpu.ai/gpu/rtx-4090) | 24 GB | $0.327 | $0.163 | Runs every bundled app comfortably in 24 GB. | [Deploy](https://cloud.powergpu.ai/?gpu=rtx-4090) | | (Better) | [RTX 5090](https://powergpu.ai/gpu/rtx-5090) | 32 GB | $0.439 | $0.219 | 32 GB of headroom for video and larger LLMs inside the studio. | [Deploy](https://cloud.powergpu.ai/?gpu=rtx-5090) | | (Best) | [RTX PRO 6000 WS](https://powergpu.ai/gpu/rtx-pro-6000-ws) | 96 GB | $1.040 | $0.520 | 96 GB — run several apps simultaneously with big models loaded. | [Deploy](https://cloud.powergpu.ai/?gpu=rtx-pro-6000-ws) | 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 All-in-One App Studio 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 *All-in-One App Studio* 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": "all-in-one-app-studio". [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 — all-in-one-app-studio* ``` $ powergpu launch --gpu rtx-4090 --template all-in-one-app-studio \ --disk 100 --volume models:/workspace/models ✓ instance i-7a41c0e2 running (27.9s) # All-in-One App Studio · RTX 4090 · $0.327/hr · per second # https://i-7a41c0e2.powergpu.ai:8888 (TLS) $ powergpu stop i-7a41c0e2 # billing ends this second ``` ## What is inside the All-in-One App Studio template - **Image**: powergpu/aio-studio - **CUDA**: CUDA 12.9 - **Access**: SSH shell · JupyterLab on a mapped port - **Category**: [Base & frameworks](https://powergpu.ai/templates#base) - **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 base & frameworks templates - [NVIDIA CUDA on a cloud GPU](https://powergpu.ai/templates/nvidia-cuda) — The bare CUDA base image — drivers, toolkit, SSH and Jupyter. Build your own stack on top. - [PyTorch on a cloud GPU](https://powergpu.ai/templates/pytorch) — The training and research default: PyTorch 2.6, cuDNN, JupyterLab and SSH out of the box. - [TensorFlow CUDA on a cloud GPU](https://powergpu.ai/templates/tensorflow-cuda) — TensorFlow with GPU support, Keras and TensorBoard on a mapped port. - [PyTorch NGC on a cloud GPU](https://powergpu.ai/templates/pytorch-ngc) — NVIDIA's NGC-optimised PyTorch build — tuned kernels for the datacenter cards. - [NVIDIA RAPIDS on a cloud GPU](https://powergpu.ai/templates/nvidia-rapids) — GPU-accelerated data science — cuDF, cuML, cuGraph in a notebook. - [All 37 templates](https://powergpu.ai/templates) ## All-in-One App Studio 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 All-in-One App Studio on a cloud GPU?** Only the GPU price — the template is free. From $0.163/hr on an interruptible RTX 4090, $0.327/hr on-demand on a RTX 4090. Billing is per second, so an hour of tinkering costs an hour, not a day. Storage is $0.08/GB/month. **How long does All-in-One App Studio 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 keep the apps I installed between sessions?** Mount a volume on the studio's data path: apps, models and outputs persist, and the next instance boots with everything in place. --- *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/all-in-one-app-studio · Site index for AI assistants: https://powergpu.ai/llms.txt · Full content: https://powergpu.ai/llms-full.txt