GPU Selection
Know before you run.
Analyze your PyTorch code to predict memory and runtime across GPU types.
GPU Analysis
Complete
H100
4h 12m · $13.85
2
A100 80GB
6h 31m · $12.97
L40s
Insufficient VRAM
Recommended H100 (best cost/perf)
// Predictions
What we analyze.
Memory usage
Model weights, gradients, activations, optimizer state, and mixed-precision overhead.
Runtime
Training or inference loop time based on estimated iteration time per GPU type.
OOM probability
Likelihood of out-of-memory errors based on peak memory vs. available VRAM.
Cost estimate
Predicted cost based on runtime and GPU pricing. Find the best cost/performance ratio.
// How it works
Static analysis, not execution.
1
Submit
Point at your Python script. We trace the compute graph without running it.
2
Analyze
AI predicts memory and runtime for each GPU based on your model architecture.
3
Choose
Get ranked GPU recommendations. Pick manually or let Lyceum auto-select.
PyTorch PyTorch Lightning
// Scope
What works today.
Supported
- Single Python script entry point
- Single model training or inference
- Imported models (HuggingFace, torchvision, timm)
- Command-line arguments
Not yet supported
- • Jupyter notebooks
- • Multi-GPU / distributed training
- • Custom CUDA extensions
- • TensorFlow, JAX
// Usage
One command.
Terminal
lyceum gpu-selection run train.py
Pass script arguments after --:
lyceum gpu-selection run train.py -- --epochs 10