> For the complete documentation index, see [llms.txt](https://docs.ai.neevcloud.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.ai.neevcloud.com/gpu-ai-service/overview.md).

# Overview

GPU AI Service allows you to deploy and manage GPU-powered compute for AI and ML workloads. You can run training, fine-tuning, batch jobs, and custom workloads without owning hardware.

The service is designed to be flexible, scalable, and easy to use for both individuals and teams.

Navigate through the GPU AI Service documentation:

* [**Getting Started**](/gpu-ai-service/getting-started.md)\
  Learn the basics of NeevCloud GPU deployment and setting up your first instance.
* [**GPU Instances**](/gpu-ai-service/gpu-instance.md)\
  Explore available GPU types, VRAM options, and how to select the right hardware for your workload.
* [**Understanding Templates**](/gpu-ai-service/understanding-templates.md)\
  Discover pre-configured environments (PyTorch, TensorFlow, Stable Diffusion) that speed up deployment.
* [**Pricing Models**](/gpu-ai-service/pricing-models.md)\
  Understand On-Demand vs. Reserved pricing to optimize your compute costs.
* [**Deploying a GPU**](/gpu-ai-service/deploying-gpu.md)\
  A comprehensive step-by-step guide to configuring, securing, and launching your GPU instance.

## Why use GPU AI Service

AI workloads require high-performance GPUs, fast networking, and reliable storage. Managing this infrastructure on your own is expensive and time-consuming.

GPU AI Service helps you:

* Start workloads quickly without long setup time
* Scale up or down based on workload needs
* Avoid upfront hardware costs
* Focus on models and code instead of infrastructure

## What GPU AI Service provides

* On-demand & Reserved GPU instances
* Multiple GPU types and configurations
* Prebuilt AI and OS templates
* Secure access and isolation per project

You can delete instances when not needed to control cost.

## Who should use GPU AI Service

* ML engineers training or fine-tuning models
* Developers running custom AI workloads
* Data scientists experimenting with datasets
* Enterprises running long-running or large-scale AI jobs
* Research teams needing flexible GPU access

## How GPU AI Service works

1. Select a GPU instance like H100, A100, V100, etc.
2. Select Instance Pricing like On-Demand or Reserved.
3. Choose an AI template
4. Attach storage if required
5. Launch the GPU instance
6. Access the instance via SSH or application endpoints

You can manage instances from the web console or programmatically using APIs.


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