Timber – Ollama for classical ML models, 336x faster than Python
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. - kossisoroyce/timber
Timber – Ollama for classical ML models, 336x faster than Python launched on March 2, 2026 with 207 votes, #153 of 23,380 launches that month and more than 99% of that year's launches.
In plain words
Timber is a development tool that runs classical machine learning models faster than Python implementations. Built as an Ollama-equivalent for ML workflows, it delivers 336x performance improvements for model inference and computation. The tool is designed for developers and data scientists who need to accelerate machine learning pipelines without rewriting their models. It integrates with existing ML stacks and provides significant speed gains for classical ML tasks.
written from the facts on this page · September 2026
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