AWS EC2 GPU instances Alternatives for Fine-Tuning
Adapting an existing base model to your own data, typically LoRA or a full fine-tune over hours.
What fine-tuning actually needs
Why people look past AWS EC2 GPU instances for this
Base weights are re-downloadable but slow; the adapter checkpoints and the dataset you spent time cleaning are not something you want to re-derive. That is exactly the gap AWS EC2 GPU instances does not close: If you already run on AWS, the GPU instances are next to your VPC, your IAM and your data, and that integration is worth real money. What you pay for it is the per-GPU rate and the fact that an EBS-backed environment is an AWS environment. It does not restore anywhere else.
To be fair, AWS EC2 GPU instances's real strength is real: Everything else in the account: VPC, IAM, S3 adjacency, compliance posture, committed-use and Savings Plan discounts, and a procurement path enterprises already have.
Live rates for the GPUs fine-tuning wants
Live per-GPU rates from Aquanode's marketplace. Refreshes hourly.
How you run it here today
Run it today with the seeded lora-finetune job recipe — a real, publicly-imaged container Aquanode can queue directly, not a template we're promising to build later.
Restoring an environment requires a snapshot that already exists. Stopping a deployment yourself captures it on the way out, so you can bring it back later on any provider. A provider-side termination is different: it is only recoverable if you had already switched automated snapshots on for that deployment, and it costs you the work since the last one. Automated snapshots are opt-in, nothing runs until you start it, and with none running there is nothing to restore.
More alternatives pages
Other workloads on AWS EC2 GPU instances
Fine-Tuning alternatives to other clouds
