Update README.md

README.md

@@ -48,6 +48,19 @@ The local inference script loads a PyTorch checkpoint, prepares the configured i
 
 When live ERA5 data is unavailable, the example inference path uses normalized atmospheric inputs as a software smoke test. Operational or scientific use requires real, correctly aligned atmospheric analysis data.
 
+## Minimum System Requirements
+
+For local inference with the released PyTorch checkpoint:
+
+- Operating system: macOS, Linux, or Windows
+- Python: 3.10 or newer
+- Processor: 64-bit CPU with four cores or more
+- Memory: 8 GB RAM minimum; 16 GB recommended
+- Storage: 2 GB free space for the repository, environment, checkpoint, and generated forecasts
+- GPU: not required; CPU inference is supported. Apple Silicon can use MPS when available.
+
+For faster experimentation, training, or batch inference, use a CUDA-capable NVIDIA GPU with at least 16 GB VRAM. An A100 or H100 is suitable for full training, but is not required to run the released checkpoint.
+
 ## Model Formats
 
 The native release format is PyTorch. GGUF is designed primarily for transformer-style language models and is not a suitable interchange format for this custom CNN-GRU probabilistic model. ONNX or TorchScript conversion may be possible, but any converted model must be validated against the native checkpoint for numerical and forecast consistency.