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Pythia analyzes the computational characteristics of your PyTorch workloads and maps them to the GPU architectures that deliver the best performance-cost efficiency. Instead of guessing which GPU you need or over-provisioning expensive resources, Pythia provides clear, evidence-based guidance on which option matches your requirements.
Pythia currently evaluates T4, A100, and H100 GPUs.

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What You Get

Run a single command and Pythia will:
  • Predict memory requirements to identify compatible hardware
  • Estimate runtime for each GPU option
  • Recommend the best GPU based on your workload

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CLI Usage

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Run GPU Selection

Pass script arguments after --:

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Options

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Check Status

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Requirements

  • Single Python script as entry point (executed as main)
  • Must target CUDA/GPU as the PyTorch device
  • Command-line arguments are supported
  • PyTorch and PyTorch Lightning
  • Imported models (Hugging Face, torchvision, timm)
  • Single model, single GPU
  • Jupyter notebooks (not supported at this point)
  • Advanced or custom gradient manipulation
  • CUDA extensions that bypass PyTorch tensors

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Prediction Details

Predicts training and inference memory for a single model. Accounts for gradients, model weights, activations, optimizers, and mixed-precision.Does not account for data transfer outside the training/inference loop or operator memory overhead (intermediate tensors).
Estimates inference/training loop time based on iteration time.Does not account for VM startup time or initial data downloading and loading.