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