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Getting Started with GPU

Deploy your first GPU instance and start running compute workloads.

Deploying a GPU Instance

  1. Log in at lexoya.com/login
  2. Choose your GPU configuration
  3. Select your software stack
  4. Deploy your instance

Using Your GPU

Check GPU Status

nvidia-smi

Run a PyTorch Example

import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"CUDA available: {torch.cuda.is_available()}")

Monitor Performance

nvidia-smi --query-gpu=utilization.gpu,memory.used,temperature.gpu --format=csv -l 5

Pricing

GPU instances are billed per second with hourly minimums. Pricing varies by GPU model and configuration. See the GPU overview for available plans and pricing in IRR.

FAQ

What GPU models are available? GPU plans include NVIDIA L4, A40, L40S, and A100 configurations. See the GPU overview for full details.
What software can I run? Any CUDA-compatible workload, including PyTorch, TensorFlow, and custom GPU applications.