High-performance ahead-of-time compiler for Machine Learning

In plain words

This is an ahead-of-time compiler designed for machine learning workloads, launched in 2018. It compiles ML models to improve execution speed and efficiency compared to standard interpretation. The tool is intended for developers and researchers working with machine learning systems who need better performance from their computational models. It focuses on compilation techniques to optimize ML inference and training operations.

written from the facts on this page · September 2026

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  TensorFlow 1.02017 · tensorflow.org · ▲823

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  Timber – Ollama for classical ML models, 336x faster than PythonMar 2026 · github.com · ▲207

  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

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  My "weekend" project, an ML dialect and compiler2012 · texodus.github.com · ▲68
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  Compile ML models into dependency-free source code for easy deployment2023 · waveworks.dk · ▲37

  I'm a machine learning engineer who always found it annoying to integrate ML models into phone apps, smartwatch apps, microcontroller firmware etc... Why do we need all these libraries and runtimes with all the overhead, compatibility issues and other headaches, when it's just some math to be executed? So I made a compiler that simply converts the model into plain source code with no dependencies, and it actually solved all my deployment problems. Now I'm curious if it can help anyone else too. Through the link you can submit your model file (Keras h5, onnx soon to be supported), and I'll…

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  Life & fun · 2018 · refactoringui.com

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