Train and run PyTorch models on an NVIDIA Tesla P40 (24 GB VRAM, Pascal architecture), with full root access. GPU servers are not orderable yet; contact us to be notified when they are.
$ pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
# Exemplum instructioe
PyTorch est framework instructionis profundae, quae in usu est. Servitor GPU PyTorch instructionem et inferencem in GPU executat, quae multo velocior est quam CPU in multis modelibus.
Installa NVIDIA driver, CUDA et PyTorch build necesse. Verificate ut PyTorch build Pascal GPUs (computationis potestas 6.1) ancora sustineat.
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Configuratio ambientis exacte cum tuo programmate necesse est.
PyTorch can use an NVIDIA GPU to speed up its work. VPS.org GPU servers are not available to order yet; contact us to hear when they are. PyTorch is a training / fine-tuning workload. Plan for long-running jobs: snapshot your VPS regularly and keep a copy of trained weights outside VPS.org.
GPU servers are not available to order yet. Today you can run PyTorch on a regular CPU plan, where it works far more slowly than on a GPU: SSH in and run pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124.
Training VRAM is dominated by the optimizer state plus activations. Full fine-tuning of a 7B LLM needs roughly 24 to 48 GB; LoRA / QLoRA fits in 8 to 16 GB.
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Yes. Every VPS.org server comes with full root access, so you can run other tools alongside PyTorch within the server's RAM and storage.
Every VPS.org server comes with full root SSH access, so you can install drivers, libraries and PyTorch however you need.
No. No server comes with a CUDA runtime or NVIDIA driver preinstalled, and GPU servers are not available to order yet. You install the versions PyTorch needs yourself.
Yes. VPS.org servers are persistent virtual machines, not ephemeral instances: PyTorch models, configs and data stay on the server's SSD storage until you delete them.
Keep working data on the server's SSD for fast access during PyTorch runs, and copy finished artifacts (weights, generations, embeddings) to storage outside VPS.org; snapshots and backups stay on the same storage cluster.
You can upgrade any VPS.org server to a larger plan from the control panel; the resize restarts it and your PyTorch install carries over. GPU servers are not available yet.
Yes. Backups are a paid add-on (daily +30% of the plan price, kept 7 days; weekly +20%, kept 28 days) and snapshots are free, which is useful before long PyTorch runs. Both stay on the same storage cluster as the server.
Contact support within 30 days of your first purchase to request a refund. GPU servers are not sold yet, so no GPU order is involved.
Capacitas GPU limitata est. Dite nos de tuae labores et respondemus cum disponibilitate et prezzo.