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Choose the template that matches the work you plan to do: Ubuntu 24.04 for a custom setup, Ollama for browser-based local language-model use, JupyterLab for notebooks, Unsloth for fine-tuning, or InvokeAI for image generation. Application templates require at least two CPU cores. Before deploying, check the template’s access settings, confirm the GPU has enough VRAM for your workload, and plan how you will export your files—templates cannot be changed in place, and instance storage is ephemeral.
Which template fits your workflow?
A template is the operating-system and application image installed when a GPU instance is deployed. Each option uses Ubuntu 24.04 as its base; application templates add a ready-made environment for a particular workflow. The details below reflect the provider’s current template guide.
| Template | Best for | What to know before choosing |
|---|---|---|
| Ubuntu 24.04 | Building a custom or unusual software stack | A bare operating system without preinstalled applications or exposed web services. You configure the environment yourself. |
| Ollama | Running local language models | Includes Ollama and Open WebUI for browser interaction. Open WebUI is exposed automatically; the Ollama API remains private to the instance. |
| JupyterLab | Notebook-based experimentation and training code | Includes PyTorch and CUDA 12.4 drivers. The notebook service is exposed automatically and protected by an access token. |
| Unsloth | Fine-tuning large language models | Includes Unsloth Studio configured for LoRA and QLoRA fine-tuning, with preset credentials. The service is exposed automatically. |
| InvokeAI | Generating images | Provides a web studio for Stable Diffusion and FLUX. Models are not preinstalled; download them through the internal Model Manager after deployment. |
Choose Ubuntu for control
Ubuntu is the flexible starting point if you need to assemble your own software, use a stack not covered by the application images, or control each installation choice. It avoids a preselected application environment, but setup and service configuration are your responsibility.
Choose Ollama for browser-based model interaction
Ollama is the most direct fit when your goal is to run a local language model and interact with it through a browser. Its included Open WebUI supplies that interface. Keep in mind that the browser interface and the model API have different exposure settings: Open WebUI is exposed, while the Ollama API stays internal.
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Choose JupyterLab for interactive code
JupyterLab suits notebook workflows such as experimentation and training code. Its image includes PyTorch and CUDA 12.4 drivers, so it starts with a configured machine-learning environment rather than an empty Ubuntu installation.
Choose Unsloth for fine-tuning
Unsloth Studio is intended for LoRA and QLoRA fine-tuning, not training a model from scratch. The provider says full fine-tuning requires a GPU with at least 48 GB of VRAM; smaller GPU instances support QLoRA. Confirm the exact model and method you plan to use before selecting hardware.
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Choose InvokeAI for image generation
InvokeAI provides a web-based image-generation studio supporting Stable Diffusion and FLUX. Since its models are not included in the image, allow for downloading the models you need after deployment.
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Check CPU eligibility separately from GPU capacity
Template availability depends on CPU cores, not on which GPU model you select. With one CPU core, the listed option is Ubuntu 24.04. At two or more cores, the application templates—Ollama, JupyterLab, Unsloth, and InvokeAI—are also available. Choose an instance with at least two CPU cores if you need one of those preconfigured environments.
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CPU eligibility does not tell you whether the GPU can handle your model. The provider’s GPU selection guide lists L40S with 48 GB VRAM, A100 80GB PCIe with 80 GB, and B200 and B200 Dedicated with 192 GB each. Adding CPU cores, instance RAM, or storage to a larger instance size does not increase GPU VRAM. Compare the workload’s memory needs with the selected GPU independently of the template; a ready-made image does not guarantee that a particular model will fit.
Review access settings before exposing a service
Application images can expose web services as soon as the instance is deployed. The default access arrangements differ, so check them before sharing an address or putting the service into use.
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- Ubuntu: No service is running by default, so service authentication is not applicable.
- Ollama: Open WebUI initially has no authentication. The first visitor can create an account; create yours promptly. The Ollama API remains private to the instance.
- JupyterLab: Access requires a token. Keep token protection enabled.
- Unsloth: The image uses preset username and password credentials.
- InvokeAI: No authentication is configured by default. Add access controls before sharing its address.
An exposed service can be reached publicly. Check which endpoints are exposed and secure them before sharing access. The provider’s guides explain how service exposure works and how to secure exposed services.
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A deployed template cannot be changed in place. If you want a different environment later, you must deploy a new GPU instance. Instance storage is ephemeral and has no backups; destroying an instance permanently removes the data stored on it. Export models, notebooks, generated images, and fine-tuned outputs to external storage before removing an instance. The provider also discusses this distinction in its guide to GPU instances and VPS storage.
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Account for hourly-credit billing
GPU instances are billed hourly through account credits. If credits run out, the instance is destroyed, which can also erase work stored on it. Keep enough credits available for the time you need, and export important files rather than treating the instance as permanent storage. Rates can change, so check the provider’s GPU setup guidance and billing and credits guide for current details.
Quick Recap
Make the choice in this order
- Match the image to the task: custom stack—Ubuntu; local LLM interaction—Ollama; notebooks—JupyterLab; fine-tuning—Unsloth; image generation—InvokeAI.
- Check the CPU allocation: application images need at least two CPU cores; Ubuntu is available with one.
- Size the GPU for the workload: verify VRAM requirements for the model and method instead of assuming the template determines capacity.
- Review exposure and credentials: understand which services are reachable and what authentication is enabled before sharing access.
- Prepare a data plan: decide where you will export work before deployment, and maintain enough account credits to avoid an unexpected instance shutdown.
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