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If you’ve ever wrestled with serving a good old XGBoost or LightGBM model in production, you know the pain. You train this tiny, efficient tree model, and then you end up wrapping it in a bloated Python service with Flask or FastAPI, babysitting dependencies, and watching your inference latency balloon way beyond what the model itself actually needs. It’s always felt like overkill.

That’s exactly the itch [Timber](https://github.com/kossisoroyce/timber) scratches. The pitch is “Ollama for classical ML models,” and honestly, it lives up to that comparison. Timber is an ahead-of-time compiler that takes your trained XGBoost, LightGBM, scikit-learn, CatBoost, or ONNX model and compiles it down to raw C99 inference code. No Python in the hot path. No runtime overhead. Just a native binary doing what tree models do best — a deeply nested pile of if/else branches running at microsecond speed.

The workflow is dead simple. You `pip install timber-compiler`, then run `timber load model.json –name fraud-detector` to compile your model, and `timber serve fraud-detector` to spin up an HTTP API on localhost. Hit it with a curl request and you get predictions back. That’s it. Two commands from a pickle file to a running inference endpoint. The API even follows Ollama’s conventions, so if you’ve used Ollama before, this feels instantly familiar.

The performance claim is bold — 336x faster than Python XGBoost inference, benchmarked on an M2 Pro with a 50-tree binary classifier. Even if real-world numbers are half that, you’re still looking at a massive improvement for latency-sensitive use cases like fraud detection or edge deployment.

Timber [popped up on Hacker News](https://news.ycombinator.com/item?id=47212576) as a Show HN post on March 2, pulling in 145 points and 27 comments. It also got picked up by [bestofshowhn.com](https://bestofshowhn.com/) with 137 points. The HN discussion was genuinely interesting — folks pointed out that traditional ML still powers a huge chunk of real production systems despite all the generative AI hype, and the creator explained that Timber specifically targets scenarios where feature vectors arrive pre-formed and you just need the prediction step to be fast.

Some commenters raised fair questions about the choice of C as a compilation target, but the response was reassuring: the generated code involves no heap allocation and no pointer arithmetic. It’s essentially a giant static if/else tree, which is about as safe as C gets.

If you’re running tree-based models in production and tired of Python’s overhead eating into your latency budget, [Timber](https://github.com/kossisoroyce/timber) is worth a serious look. It’s one of those tools that makes you wonder why nobody built it sooner.