> ## Documentation Index
> Fetch the complete documentation index at: https://docs.hivenet.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Choosing between GPU and vCPU instances

> Learn the difference between GPU and vCPU instances in Compute with Hivenet and when to use each type.

When creating a Compute instance, you can choose between GPU-powered and vCPU-only setups. Both work the same way in the console, but they’re built for different types of workloads.

This guide helps you decide which one fits your needs.

## The short answer

* **Choose a GPU** if you need parallel processing power for heavy workloads like AI, rendering, or simulations.
* **Choose a vCPU** if you’re running general-purpose applications, background tasks, or smaller workloads that don’t benefit from GPU acceleration.

## Key differences

| Feature          | GPU instance                                        | vCPU instance                                     |
| ---------------- | --------------------------------------------------- | ------------------------------------------------- |
| Best for         | Machine learning, inference, rendering, simulations | Web apps, APIs, CI/CD, databases, background jobs |
| Processing style | Parallel (many small cores at once)                 | Serial (fewer, faster cores)                      |
| Speed            | Faster for math-heavy, parallel tasks               | Smoother for general-purpose tasks                |
| Cost per hour    | Higher (specialized hardware)                       | Lower (cost-efficient)                            |
| Availability     | Limited by GPU supply                               | Generally more available                          |

## When to use GPU instances

Pick a GPU instance if your workload depends on massive parallelism or requires specialized acceleration. Examples:

* Training or fine-tuning AI/ML models
* Running inference with frameworks like vLLM
* Video rendering and encoding
* Scientific modeling or simulations
* Any workload that runs faster with GPU acceleration

## When to use vCPU instances

Pick a vCPU instance if your workload doesn’t benefit from GPU acceleration. Examples:

* Hosting lightweight web servers or APIs
* Running development and test databases
* Automating builds and deployments (CI/CD pipelines)
* Running scripts and background jobs
* Always-on services that need to stay cost-efficient

## How to decide

Ask yourself two questions:

1. **Does my workload rely on parallel processing?**\
   If yes → GPU is usually better.
2. **Do I need the lowest cost for general-purpose compute?**\
   If yes → vCPU is the simpler and cheaper option.

<Tip>
  You don’t need to commit forever. You can start with vCPUs for development and testing, then move to GPUs when you’re ready to train or deploy heavier workloads.
</Tip>