This paper presents the theoretical foundation for a new class of computation: Tetrahedral Computing. We describe the transition from probabilistic stochastic models to a deterministic geometric architecture that provides a hardware-independent exact-acceleration of 555x. We demonstrate that offloading the computational burden from neural network weights to a geometric core enables sub-millisecond response times on standard hardware. This architecture paves the way for sovereign AI systems based on next-generation architectures, such as the NVIDIA RTX Spark (N1X).
Zyabin et al. (Sat,) studied this question.