ZETAPHI

Developing model architectures that move beyond quadratic attention.

Developing attention-replacement architectures for custom models with linear scaling and compute-speed advantages without sacrificing capability.

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Custom models Linear scaling Evidence-bounded claims

WHAT ZETAPHI IS DOING

Architecture work aimed at speed, scale, and deployment reality.

ZETAPHI focuses on model architecture directions for settings where throughput, memory, and compute efficiency matter.

Linear-scaling direction

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.

Internal configuration as the quality axis

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. Across our benchmark series, internal configuration strength is the dominant quality axis — with the strongest configuration cutting the dense Transformer's error by roughly 42% on structured sequence tasks, at no additional parameter cost and no added latency.

Deployment on real hardware

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

Mark and direction chosen for clarity and technical tone.