Show HN: Timber – Ollama for classical ML models, 336x faster than Python (github.com)

Timber has emerged as a game-changing tool for the AI/ML community by enabling the compilation of classical machine learning models, like XGBoost and LightGBM, into optimized native C binaries. This process drastically improves inference speeds, achieving a remarkable 336 times faster performance compared to Python-based XGBoost when using a single-sample prediction benchmark. Designed for efficiency, Timber facilitates low-latency inference across various applications, especially for teams in fraud detection, edge computing, and regulated industries such as finance and healthcare, where fast and reliable model responses are crucial. The framework streamlines the deployment of machine learning models by eliminating the Python runtime from the inference path, resulting in microsecond latency and a small footprint for deployed models. Timber supports a range of model formats including ONNX and CatBoost, with an easy-to-use command structure that allows users to load and serve models swiftly. The release comes with extensive benchmarking capabilities and documentation, illustrating Timber's potential to enhance model portability and compliance across diverse deployment environments, making it a significant advancement for developers and organizations seeking scalable and efficient ML solutions.