Timber AOT compiler converts XGBoost, LightGBM, scikit-learn, CatBoost and ONNX ML models into native C99 code; achieves 336x faster inference than Python with microsecond latency for edge, IoT and low-latency deployments

GitHub - kossisoroyce/timber: Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference.

Timber Ollama for classical ML models. Timber compiles trained tree-based models (XGBoost, LightGBM, scikit-learn, CatBoost, ONNX) into optimized native C and serves them over a local HTTP API. No Python runtime in the inference hot path Native latency (microseconds) One command to load, one command to serve 📚 Docs: https://kossisoroyce.github.io/timber/ Who is this for? Timber is built for teams that need fast, predictable, portable inference: Fraud/risk teams running classical models in low-l...