Why we build our own C and C++ inference engines
Why we write our own C and C++ inference engines
LocalAI's team explains why they sometimes write custom C/C++ ports of AI models instead of wrapping existing engines. The payoff: a 66 MiB binary that matches vLLM's throughput and memory footprint, and a CPU port that beats PyTorch 1.31x on depth estimation. But the real gate is parity—outputs must match the reference exactly—and the cost is heavy maintenance, especially on GPU kernels.
A port that is fast and slightly wrong is worthless, and without a per-component gate you find out it is wrong months later.
- dennis16384
I had a similar success with Model2Vec static embedder and NER inference (both GGUF, compiled for WASM), ported to plain C from ONNX Runtime.
Wasm size from 30Mb to 300kb and 1.5x speedup. It's definitely worth it for performance or distribution size.
- stephbook
Should have started with writing your own blog posts.
- adithyassekhar
What you get: X is the A, Y is the B.