Hyper-Spatial Neural Rendering: Zero-GPU & On-Device GenAI via Large Prime Coordinates

While state-of-the-art Generative AI models demonstrate exceptional performance, they face the dual challenges of "The Memory Wall" and excessive carbon emissions. This paper proposes a paradigm shift from "Probabilistic Token Generation" to "Deterministic Coordinate Rendering." By mapping LLM knowledge into a high-dimensional lattice defined by Large Primes, we present a "Stateless and Zero-GPU" architecture. This approach theoretically reduces power consumption by 99.9% and completely eliminates the need for expensive H100 clusters, collapsing the OPEX of AI services. Ultimately, this research marks a fundamental divergence in the AI paradigm: While the industry optimizes Probability, we propose Determinism. While others refine GPU Efficiency, we realize Zero-GPU.