I recently attended a Gartner webinar where they were discussing offloading activities from LLMs. Given the lower energy requirements and the one-shot learning capabilities of functor models I came up with the Global Network Model (GNM). This is preliminary work but it suggests an alternative to scaling for the LLM. Here agents work with functor micro models to discover new data. That data goes to the data processor for cleansing, dedups, canonicalization, learning preparation, etc. Then the learning router submits the learning unit to the appropriate SLM. The LLM can invoke inference on any SLM to gain knowledge, generate code, etc. The Graph Neural Networks are contained in a new type of ensemble that allows messaging between nodes in different models using an orchestrator. 

This can be an alternative to adding transformers and using GPUs in certain cases. Lower energy consuming functor models can support a reduction in overall LLM energy usage and cost. Of course this is preliminary and new research. See this document for further details.

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John Harby
CEO
Autonomic AI, LLC
Temecula CA
https://www.autonomicaillc.com
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