Machine learning models in production face a fundamental constraint: you cannot simultaneously maximize accuracy, minimize inference latency, and minimize memory usage. This trade-off forces every system to choose a corner of the triangle, moving beyond academic benchmarks to real-world constraints like latency budgets and RAM limits.

High accuracy demands more parameters and stored data, which consumes memory. However, large memory footprints cause cache misses and random access, hurting inference speed. Conversely, optimizing for speed requires predictable code paths and contiguous memory, which reduces model flexibility and can lower accuracy. This is a systems problem, not a tooling one.

Popular methods exemplify these choices. Brute-force KNN prioritizes accuracy but is slow and memory-heavy. Tree-based models balance speed and accuracy but use significant memory. Linear models are fast and memory-efficient but limited in expressiveness. No single model wins all three; each is optimized for a specific production corner.

The real design question isn't which model is best, but which corner your system must occupy. Online ads prioritize speed, fraud detection needs accuracy, and edge devices balance speed and memory. Good ML engineering means intentionally choosing this trade-off and understanding the physics of memory, computation, and information on real hardware.