Timber
Ollama for classical ML models, 336x faster than Python
Details
- External ID
- 47212576
- Source
- HN
- Company
- —
- Product
- Timber
- Website domain
- github.com
- Launched
- March 2, 2026
- Cohort
- —
- Upvotes
- 207
- Upvotes percentile
- 0.966789667896679
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Enrichment
- Theme
- ML inference and model optimization
- Vertical
- Horizontal
- Function
- Model & infra
- Audience
- Developer
- AI stance
- Not AI
- Project type
- Commercial product
- Normalized one-liner
- fast runtime for classical machine learning models
- Manually corrected
- False
Could you build this?
No Re-implementing a high-performance C++ or Rust inference engine for classical ML models that is hundreds of times faster than Python requires specialized systems and SIMD optimization skills.
What it would actually take: Building an Ollama equivalent for classical ML requires writing a native binary runtime (in Rust, C++, or Zig) that parses serialized model weights (GBDTs like XGBoost/LightGBM, Random Forests, Linear models) into cache-aligned decision trees with SIMD/AVX vectorization and concurrent thread pools, accompanied by a model registry and unified REST/gRPC API. This requires deep low-level systems engineering, cache-locality optimization, and knowledge of classical ML internal data structures.
Discussion
20 comments analyzed.
Competitors mentioned: ONNX, xnnpack, lightgbm, llama-cpp, vLLM
Concerns raised: Swapping backends in production can be far from trivial, Feature extraction/transformation step is often the bottleneck, not inference, Only useful if you already have solved data plumbing and optimized pipeline, C compiler toolchain not available on most Unix systems, Requires pre-formed feature vectors as input
Feature requests: Accept raw input (before feature extraction) instead of just feature vectors, Add performance comparisons vs vanilla inference latencies, Support for numeric data feature extraction with LLM
Competitors
Other products that read as similar to this one — 1144 launches clear the similarity bar, closest 8 shown.
Attention rank: #50 of 1145 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 124 days after the earliest competitor.
- JevGym · github · 2026-09-22 · 27 upvotes · similarity 0.58
- OM Core · hn · 2026-06-30 · 14 upvotes · similarity 0.55
- Goku · hn · 2026-07-15 · 9 upvotes · similarity 0.55
- genpark-dense-feedforward-mlp-backprop-skill · github · 2026-09-28 · 7 upvotes · similarity 0.54
- genpark-dense-feedforward-mlp-backprop-skill · github · 2026-09-28 · 7 upvotes · similarity 0.54
- vllm-jev · github · 2026-09-24 · 72 upvotes · similarity 0.54
- XLA-based array computing framework for R · hn · 2026-03-09 · 15 upvotes · similarity 0.53
- Free Inference Engineer and Model Training Roadmap · hn · 2026-08-24 · 16 upvotes · similarity 0.53
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a model & infra tool for Fintech yet.