Timber compiler pushes classical ML into microseconds

Timber is interesting because it treats model serving like a compiler problem, not a framework problem. That makes it more relevant to infra-minded ML teams chasing deterministic low-latency inference than to the usual LLM app crowd.

• –The core pitch is strong: compile once, remove Python overhead from the hot path, and serve predictions from a tiny native artifact with zero runtime dependencies
• –The repo’s published benchmark is impressive at roughly 2 microseconds single-sample latency and about 336x faster than Python XGBoost, but it measures in-process inference rather than full network request latency
• –Timber now looks broader than a tree-only niche tool: the latest release adds ONNX linear models, SVMs, normalizers, and scalers alongside embedded deployment targets
• –The real competitive angle is against ONNX Runtime and Treelite, with Timber differentiating via an optimizing compiler pipeline plus a built-in Ollama-style serving layer