Timber: AOT Compiler Converts Classical ML Models to Native C99 Code for High-Performance Inference
By
kossisoroyce
A baker's-dozen of insight crammed into one ring.
Summary
Timber is an open-source tool that compiles classical machine learning models (XGBoost, LightGBM, scikit-learn, CatBoost, ONNX) into native C99 inference code with zero runtime dependencies. It features an AOT (ahead-of-time) compiler that optimizes models and includes a built-in HTTP server with Ollama-compatible API for serving models. The tool claims 336x faster inference than Python and microsecond-level latency for single-sample inference.
Key quotes
· 4 pulledTimber takes a trained ML model — XGBoost, LightGBM, scikit-learn, CatBoost, ONNX (tree ensembles, linear models, SVMs), or a URDF robot description — runs it through a multi-pass optimizing compiler, and emits a self-contained C99 inference artifact with zero runtime dependencies.
A built-in HTTP server (Ollama-compatible API) lets you serve any model — local file or remote URL — in one command.
~2 µs single-sample inference
336x faster than Python inference
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