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Choose the template that matches what you plan to do: Ubuntu 24.04 for a custom setup, Ollama for local language models, JupyterLab for notebook work, Unsloth for LLM fine-tuning, or InvokeAI for image generation. The application templates require at least two CPU cores. Before deploying, check the template’s access settings and export plan: you cannot change a deployed template in place, and instance storage is ephemeral.
Match the template to your task
A template is the operating-system and application image installed when the GPU instance is deployed. Every option uses Ubuntu 24.04 as its base; application templates add a prepared software environment. The right choice depends on your workflow, not on a single template being best for everyone. Hostinger’s template guide describes these options:
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| Template | Best for | What to know |
|---|---|---|
| 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 through a browser | Includes Ollama and Open WebUI. Open WebUI is exposed automatically; the Ollama API remains private to the instance. |
| JupyterLab | Notebook-based experiments and training code | Includes a web notebook environment configured with PyTorch and CUDA 12.4 drivers. The service is exposed automatically. |
| Unsloth | Fine-tuning language models | Includes Unsloth Studio configured for LoRA and QLoRA. It is exposed automatically with preset credentials. Hostinger says full fine-tuning requires a GPU with at least 48 GB of VRAM; smaller instances support QLoRA. |
| 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. |
Check whether the template is available for your instance size
Template availability depends on CPU cores, not on the GPU model. Hostinger lists Ubuntu 24.04 for a one-core allocation. With two or more CPU cores, Ubuntu 24.04, Ollama, JupyterLab, Unsloth, and InvokeAI are available. If you need an application template, select an instance with at least two CPU cores before deployment. The template guide provides the current eligibility details.
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Choose GPU memory separately
CPU cores determine which templates you can select; GPU VRAM determines how much model or workload can fit on the GPU. One does not substitute for the other. Hostinger’s current GPU selection guide lists these options and VRAM amounts:
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| GPU option | VRAM |
|---|---|
| L40S | 48 GB |
| A100 80GB PCIe | 80 GB |
| B200 | 192 GB |
| B200 Dedicated | 192 GB |
Hostinger says adding CPU cores, instance RAM, and storage through a larger instance size does not increase the GPU’s VRAM. Its Unsloth note specifies at least 48 GB VRAM for full fine-tuning and says smaller GPU instances support QLoRA. For other work, check the memory requirements of the specific model and task rather than assuming the template guarantees sufficient capacity. See Hostinger’s GPU selection guide.
Review authentication before exposing a service
Application templates can make web services reachable outside the instance. Their default protections differ, so check the relevant endpoint before sharing its address. Hostinger lists these defaults:
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- Integrated with 16GB GDDR7 256bit memory interface
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- Ubuntu 24.04: No running service, so authentication does not apply.
- Ollama: Open WebUI has no authentication until the first visitor creates an account. The Ollama API remains private to the instance.
- JupyterLab: Requires an access token.
- Unsloth: Uses preset username and password credentials.
- InvokeAI: Has no default authentication; configure access controls manually.
Hostinger recommends creating the Open WebUI account promptly, retaining JupyterLab’s token protection, and adding access controls before sharing an InvokeAI address. For service exposure and security details, consult its service exposure guide and guide to securing exposed services.
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Plan how you will preserve your work
A deployed template cannot be changed in place. To use a different one, deploy a new GPU instance. Hostinger characterizes GPU instance storage as ephemeral and says it is not backed up; destroying an instance permanently removes the data stored on it. Export models, notebooks, generated images, and fine-tuned outputs to external storage before removing the instance. Hostinger’s template guide and instance comparison guidance describe these limitations.
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- Powered by the NVIDIA Blackwell architecture and DLSS 4. System Requirements: Minimum 850W PSU with 16-pin 12V-2x6 (12VHPWR) connector required. Verify before purchasing.
- Military-grade components deliver rock-solid power and longer lifespan for ultimate durability. Compatibility: 348mm (13.7") length, 3.6 slots, 4.3 lbs. Confirm case clearance and slot spacing. GPU bracket included.
- Protective PCB coating helps protect against short circuits caused by moisture, dust, or debris
- 3.6-slot design with massive fin array optimized for airflow from three Axial-tech fans
- Phase-change GPU thermal pad helps ensure optimal thermal performance and longevity, outlasting traditional thermal paste for graphics cards under heavy loads
Keep the billing model in mind
GPU instances are billed hourly through account credits. Hostinger says an instance is destroyed if its credits run out, which can remove work kept on the instance. Maintain enough credits for the time you expect to use it and export important files independently. Rates can change, so check the current billing and credits guidance before estimating costs; the GPU setup guide explains deployment.
Quick Recap
Best Value
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- Powered by GeForce RTX 5060
- Integrated with 8GB GDDR7 128bit memory interface
- PCIe 5.0
- WINDFORCE cooling system
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- AI Performance: 767 AI TOPS
- OC mode: 2632 MHz (OC mode)/ 2602 MHz (Default mode)
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- Axial-tech fan design features a smaller fan hub that facilitates longer blades and a barrier ring that increases downward air pressure
- A 2.5-slot design maximizes compatibility and cooling efficiency for superior performance in small chassis
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