ZETAPHI
Developing attention-replacement architectures for custom models with linear scaling and compute-speed advantages without sacrificing capability.
WHAT ZETAPHI IS DOING
ZETAPHI focuses on model architecture directions for settings where throughput, memory, and compute efficiency matter.
01
Traditional transformer attention becomes the bottleneck as context length grows, because every token must compare against every other token, creating an O(n²) cost in memory and compute. Our architecture replaces that quadratic attention pattern with a linear-scaling mechanism, allowing long-context learning models to process more information efficiently without attention costs exploding as sequence length increases.
02
Standard models attempt to resolve all data simultaneously through a single, monolithic matrix, often blurring complex signals into probability noise. Our engine divides relational processing across a "committee" of discrete, structurally biased witnesses. By hierarchically reconciling these distinct viewpoints, the architecture filters out noise and phase-locks onto the true underlying geometry of the data.
03
By fundamentally decoupling intelligence from massive memory allocation, our architecture achieves state-of-the-art sequence modeling on standard consumer hardware. What traditionally requires massive, billion-dollar data centers to process can now be mapped natively and efficiently on local GPUs. We are breaking the compute bottleneck, making true, uncompromised long-context AI accessible and highly scalable.
BRAND SYSTEM
PRIMARY
