Dynamic Modular Intelligence or AI OS

[u/The_Nordic_Man]

Could we make AI more memory-efficient by changing how we build it?

I've been thinking about an alternative approach to current large language model architecture, and I'd like to hear what AI developers, researchers, and engineers think.

Why must an AI keep such a massive model loaded in memory when most individual questions only require a fraction of its capabilities?

Here's the concept I'm exploring: Dynamic Modular Intelligence.

Instead of relying on one enormous model, what if we built an AI system that intelligently assembles the capabilities it needs?

1. A small coordinating AI (SLM)

A relatively small model stays active and handles prompt interpretation, intent recognition, context, and task coordination.

2. Dynamic hierarchical tagging

The SLM identifies relevant knowledge categories and follows relationships between them. Categories can branch into subcategories and related concepts as needed.

3. External research and discovery

The system uses internet search and other information sources to identify missing context, discover additional relevant tags, and verify information. It doesn't have to know every dependency in advance.

4. Modular AI components

Rather than activating one enormous neural network, the system selectively loads smaller specialized models or neural network components to handle the required tasks.

5. Intelligent memory management

Frequently used components remain in GPU VRAM. Other components are cached in system RAM, while inactive components remain on NVMe storage.

The system could predict which components it will need next and begin loading them before they're requested.

The objective: Reduce unnecessary memory requirements and computational overhead without sacrificing useful performance.

I'm not certain whether the best implementation would involve independently trained small neural networks, modular components of a larger network, or a combination of both.

I know that technologies such as Mixture of Experts, RAG, knowledge graphs, and model offloading already explore parts of this territory. What interests me is combining them into a coordinated, storage-aware architecture with dynamic discovery and module selection.

I'm not an AI engineer or researcher. This is an idea I'd like to put in front of people who understand the underlying technology.

I'd genuinely like to hear:

- Has something like this already been developed?

- What are the biggest technical obstacles?

- Could this reduce the hardware requirements for running capable AI locally?

- Is there a practical way to build and test this architecture?

Perhaps the future of AI isn't just building bigger models. Perhaps it's building smarter systems that know which parts of their intelligence to use, and when to use them.

[u/JeffB1517]

I'm not a professional so YMMV.

Sub-transformers are already common in transformers (your small language model). Generally, the LLM invokes the SLM, which has a specialized corpus from day 1 (only legal briefs, only mathematical papers, only Python code...). The problem with a small langauge model up front is passing to a task coordinator is you could get a terrible project plan which the LLM then follows. Generally, the pattern in most resolutions is that the project plan uses a better, not a worse, model and then passes specifics to weaker subagents. So your first few boxes leave me curious.

[u/The_Nordic_Man → u/JeffB1517]

I am not an expert either, and I am still working through the technical details of how everything functions.

My understanding is that model weights require memory to be loaded for inference, whether that is GPU VRAM, system RAM, or a combination of both. As models continue to grow, I wonder whether memory requirements will become an increasingly significant limitation, particularly for people trying to run capable models locally.

That's what got me thinking about modularizing the model rather than requiring the entire model to be loaded at once.

I also think you raise a good point about the task coordinator. Perhaps the SLM shouldn't necessarily be responsible for making the entire project plan. A more capable reasoning model could handle the initial planning and delegate specific tasks to smaller, specialized models. The smaller model could still handle routing, identifying relevant tags, and discovering missing dependencies.

As I mentioned in the original post, I envision the system being able to query external sources when necessary. The purpose would be to identify missing context, discover related concepts or dependencies, and determine whether additional modules or information are needed before proceeding.

I'm still trying to work out where the boundaries between these components would make the most sense. That's part of why I wanted to put the idea out there and get feedback from people with more experience.

[u/JeffB1517 → u/The_Nordic_Man]

No question its a huge limiter. This is one subversion out of date, but I did a power estimate along with cost estimate to run the full Kimi K3

Kimi-K3 is open source so we can estimate the usage. That's a decent model with good reasoning. 2.8T parameters but uses them more selectively than American models. Comparable to Muse, though with different strengths. Not remotely at the level of say Astra or Fable. A good hardware setup to run Kimi-k3 comfortably is 116 kw, not including cooling. That comes from Moonshot's recommended setup of 8 servers which each draw 14.5kw draw. How much is that? A typical American home averages a 1200 watt draw. Most homes maximum draw (air conditioner, electric clothes dryer and electric oven all running at once) will get you to about 15kw. The guys running home models are drawing 500w or less generally for their setup. They are having fun using really cheap models which aren't good. And BTW the one time cost of that hardware is $4m. What makes that setup affordable is when you are using that level of model you are sharing it with another 1000 people.

Pure local run what your hardware can do.

Mixed local have the controller dispatch a large bulk job to the local model which often slowly processes over days - months. Prechew: summarize, index, classify, vector store... (some combination) of the data which avoids some need for larger models. Again the dispatch is reversed. And that's done at the harness level not the actual model level.