Azure GPU VMs Alternatives for Training
Training a model from scratch or continuing a pre-training run, usually for days rather than hours.
What training actually needs
Why people look past Azure GPU VMs for this
The checkpoint is the cheap part to move; the built environment, the dataset cache and the exact CUDA and framework versions around it are what take a day to rebuild. That is exactly the gap Azure GPU VMs does not close: Azure's NC and ND series make sense when your organisation is already on Azure and the GPU needs to sit inside that boundary. Priced and operated as enterprise cloud; the environment is an Azure managed disk and does not leave Azure.
To be fair, Azure GPU VMs's real strength is real: Enterprise integration: Entra ID, Azure networking, compliance certifications, and existing enterprise agreements.
Live rates for the GPUs training wants
Live per-GPU rates from Aquanode's marketplace. Refreshes hourly.
How you run it here today
Run it today as a Pod: the console's own box workload card preselects the right template for this job.
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 Azure GPU VMs
Training alternatives to other clouds
