Modular LLM framework inspired by Linux – aiming for a one-GPU future

I want to share a concept I've been thinking about, which I call *AI-Kernel*.

The idea is to manage large language models (LLMs) like we manage the Linux kernel: - A *stable, long-term maintained base model* (the "kernel") - Modular fine-tuned components (LoRA) as "patches/extensions" - A public registry of LoRA modules, with ratings and metadata - Flexible loaders (Ollama, llama.cpp, vLLM) to run the kernel + LoRAs - A unified frontend (React/JS or CLI) to interact with the system - Fully local or cloud, depending on user choice

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### Why?

LLMs are growing in size, cost, and opacity. Instead of bigger and bigger models, what if we focused on *efficiency, modularity, and sustainability*?

This proposal suggests a benchmark for AI sustainability:

> If GPT-5 runs on 10,000 GPUs in 2025, > then GPT-4 should run (with all features intact) on a *single GPU in 2026* – even if slower. > In 2027, GPT-5 should become the single-GPU target.

Always *one generation behind, but fully local and sovereign*.

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### How it works

[ AI-Kernel (base LLM) ] |

+----------+----------+ \| | | \[ LoRA A ] \[ LoRA B ] \[ LoRA C ] ← Modular specialization | \[ Loader (Ollama / llama.cpp / vLLM) ] | \[ Frontend UI (web / desktop) ] | User

LoRAs are small, stackable, and don't alter the base model. Like VS Code extensions, they can be published, rated, shared, and combined.

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### Transparency

I’m *a self-taught developer*, not an AI researcher. This is not a working product or codebase — just a structured idea for discussion.

Maybe others already thought of it. Maybe I’ve missed limits or blockers. But I wanted to write it down clearly and let more qualified people refine or challenge it.

This draft was co-written with GPT, in full transparency. The vision is mine; the wording was assisted.

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### What this is NOT

- Not a fork or fight against existing projects - Not an implementation with code (yet) - Not claiming novelty or exclusive ownership

It’s simply a *direction to consider*: A modular, open, kernel-like model for AI that is sustainable and private.

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### Call to action

If this resonates with you: - Improve it - Challenge it - Build loaders, registries, or LoRA modules - Or just ignore it if you think it’s irrelevant

We don’t need dozens of forks of LLMs. We need *one clean foundation, and thousands of flexible adaptations*.

Let’s build it — together. ```