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 resolve every pairwise interaction through a single dense attention matrix, which grows quadratically with context and can wash out structured long-range signals. Our architecture distributes relational processing across a committee of structurally distinct internal pathways and reconciles their outputs hierarchically. Across our benchmark series, strengthening that internal structure is the dominant quality axis — at zero additional parameters and zero added latency.
03
The architecture is built for settings where memory and latency budgets are real constraints: edge sensors, embedded controllers, and consumer GPUs. In our measurements it holds flat batch-1 inference latency as sequence length grows, sustains million-token forward passes on a single consumer GPU, and degrades gracefully under sensor faults behind standard driver filters — the properties that decide whether long-context models actually ship.
BRAND SYSTEM
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