On2Ai Systems — Architecture Blueprints

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Topics covered on this page: Centralized, Intelligence, ., Distributed Applications..

On2Ai Systems Architecture Blueprints # Centralized Intelligence . Distributed Applications. A ground-up custom-designed architecture framework where a shared AI model is deployed as a replicated swarm service, extending inference across every connected application while compute capacity grows with the cluster. This is our core architecture model for AILocal and most AICustom frameworks. Explore the architecture View proprietary patterns View Unlimited AI on Premises Core Architecture ## Centralized Intelligence Model Architecture Framework One shared intelligence layer, many distributed execution points, and a common inference fabric for the entire On2Ai application ecosystem. The Centralized Intelligence Model Architecture Framework is a ground-up distributed AI architecture in which a shared AI inference model is deployed as a replicated service across a Docker Swarm cluster and exposed to authorized applications on the swarm network. Rather than embedding and operating a separate AI model inside every application, all On2Ai applications connect to a common intelligence layer for inference. As the swarm expands, additional model replicas can be distributed across eligible nodes, allowing inference capacity to scale horizontally while remaining available to all connected applications. Architecture Principles ## Designed to grow with the swarm. The framework separates application behavior from model execution so products can evolve independently while sharing a scalable intelligence foundation. ### Centralized Intelligence Applications share a common AI inference layer instead of duplicating model hosting and lifecycle operations. ### Distributed Execution Model replicas execute across multiple swarm nodes while presenting one logical intelligence service. ### Horizontal Scalability Inference capacity grows as worker nodes and service replicas are added to the cluster.…

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