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
# Example install command
PyTorch is a widely used deep learning framework. A GPU server runs PyTorch training and inference on the GPU, which is far faster than on a CPU for most models.
You install the NVIDIA driver, CUDA and the PyTorch build you need. Check that your PyTorch build still supports Pascal GPUs (compute capability 6.1).
Room for small and mid-sized models, LoRA fine-tuning and moderate batch sizes.
Run Jupyter notebooks on the server for interactive work on the GPU.
Configure the environment exactly as your project needs.
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.
GPU pricing will be published when GPU servers can be ordered. Contact us if you want to hear when that happens.
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.
GPU servers are not orderable yet. Tell us about your workload and we will let you know when they are.