Network Programming in .NET

One of the biggest challenges in scaling AI is that large language models (LLMs) are monolithic. Training them requires vast clusters of GPUs working in lockstep, because the model is a single, dense network where every weight can, in principle, interact with every other. That makes it hard to imagine breaking down training into smaller jobs that could run independently on, say, thousands of consumer-grade GPUs scattered around the world.

But is that strictly necessary? If a model generates Shakespearean sonnets and also writes Python code, do the “Shakespeare neurons” really need to interact with the “Python neurons”? Or could we identify relatively independent regions of the network and train them separately?

Why LLMs Are So Entangled

Modern transformers are designed around dense connectivity. The input embeddings, attention layers, and feedforward blocks are all built so that any token can, at least in theory, influence any other. Add to that the fact that natural language and code often overlap (think “explain this function in English”), and you start to see why training is typically done as a single, inseparable job.

Where Modularity Does Appear

Despite this apparent entanglement, modularity emerges naturally:

Specialized attention heads. Researchers have found heads that reliably focus on tasks like copying, punctuation, or number tracking. Sparse activations. For any given prompt, only a small fraction of neurons fire strongly. A Python snippet activates a different pattern than a sonnet. Mixture of Experts (MoE). Some modern models explicitly enforce modularity: instead of one giant dense block, they maintain a collection of “experts,” and only a handful are activated per token. This allows scaling up the number of parameters without scaling up the compute proportionally.

In other words, while transformers are dense by design, their behavior is often sparse and task-specific.

Could We Train Modules Independently?

Here’s the big idea: if we could reliably identify which parts of a model specialize in poetry versus code versus math, we might be able to train those modules separately and then stitch them together. Some possible approaches:

Activation clustering. Track which neurons/heads fire for certain types of data, and group them into “modules.” Progressive freezing. Train one module on code, freeze it, then train another on poetry. Orthogonal subspaces. Regularize the network so that different domains live in distinct representational spaces.

This would make it feasible to break training into smaller jobs, perhaps even distributed across heterogeneous compute.

The Catch

The problem is that language domains aren’t truly separate. Shakespearean sonnets and Python functions both require reasoning about syntax, analogy, and structure. If we isolate them too aggressively, we risk losing valuable cross-domain synergies. Coordination between modules—deciding which to use at inference time—is also non-trivial. That’s why today’s modular approaches (like MoE or adapters) still rely on a shared backbone and careful routing.

Where Research Is Headed

Instead of trying to cut apart existing dense models, most progress is happening in designing modular architectures from the ground up:

Mixture of Experts for scalable modularity. Adapters and LoRA for lightweight, domain-specific fine-tuning. Compositional networks where different modules explicitly handle different domains and a top-level router decides which to use.

These ideas all echo the same insight: giant monolithic models are powerful but inefficient, and the future likely lies in more modular, sparse, and independently trainable systems.

Closing Thought

The dream of distributing LLM training across a swarm of consumer GPUs hinges on finding the right balance between shared generalization and domain-specific modularity. We’re not quite there yet, but the direction is clear: the next generation of AI systems won’t be monoliths. They’ll be federations of specialized experts, stitched together into something greater than the sum of their parts.