GPU hosting for training and fine-tuning
Fine-tuning a model and training one from scratch are different workloads. Start with a reproducible training configuration and a recovery plan.
Editorial guide updated 2026-10-09. Provider facts and prices carry their own dates.Understand the training configuration
Dataset size, sequence length, batch size, optimiser and training method influence resource needs. Use the framework's documentation and a short trial rather than assuming a model's inference requirements apply to training.
Protect checkpoints and datasets
Check persistent storage, upload time and checkpoint recovery. If an instance can be interrupted, confirm how much work would be lost and how quickly you can resume.
Treat multi-GPU separately
Total VRAM across several GPUs is not automatically equivalent to one GPU with that memory. Verify distributed-training support, interconnect requirements and the provider's actual topology before committing.
Before you choose
- Run a short training trial.
- Verify checkpoint recovery.
- Confirm topology when using multiple GPUs.
Matches use published catalogue evidence. Unknown specifications, unsupported workflows and current inventory must be confirmed with the provider. This guide is not a performance benchmark.
Keep exploring
VRAM requirements · Pods versus serverless · Estimating costs · Compare in ChatGPT