When to use GPU vs CPU hosting for AI workloads
A practical guide to choosing between GPU rental and VPS hosting for AI workloads. When to use GPU vs CPU for training and inference.
When GPUs make sense
Training models — GPUs parallelize matrix math and dramatically speed up training. A100/H100 instances can cut training time from days to hours.
Batch inference — Processing thousands of samples at once benefits from GPU parallelism.
Large models — Models that don't fit in CPU memory or that are too slow on CPU are good candidates for GPU rental.
When CPU (VPS) is enough
APIs and web apps — Many production APIs serve one request at a time. A beefy VPS can handle moderate throughput with lower cost than a GPU.
Preprocessing and orchestration — Data loading, tokenization, and job scheduling often run fine on CPU.
Small or quantized models — Tiny models or heavily quantized ones can run on CPU with acceptable latency.
At OneRaap we offer both: spin up a GPU when you need it for training or batch jobs, and use VPS for everything else. You only pay for each when it's running.