Large Language Models: Language at a Scale

We’ve come a long way—from models with just a few million parameters to ones with billions or trillions, almost without a second thought. And why not? Bigger models understand language better, generate more detailed responses, and handle a wider range of tasks. But here’s the thing: scaling up by adding more parameters is like running a massive power plant—impressive but incredibly expensive. The real challenge is finding a way to scale smarter, not just bigger.

We will explore three clever ways to push beyond raw scale, making models more powerful, efficient, and useful in real-world scenarios. Together, these techniques aren’t just about making models larger—they’re about making them smarter, more capable, and more practical. So, let’s dive deeper into these exciting methods for scaling large language models beyond raw parameter counts!

What is a mixture of experts (MoE) model?

A mixture of experts model consists of multiple expert networks plus a router:

• Experts: Sub-networks trained to handle certain input aspects (like math, code, or everyday language).

• Router: A gating mechanism that decides which experts to use for each token or input segment.

Instead of sending every token through every parameter, the router picks just the top one or two experts for that token. Your total capacity is huge (sum of all experts), but per-token compute is only a slice of that total. It helps because if we only run 10% of our parameters for each token, we slash the time and memory needed at inference. Also, each expert can excel at a sub-problem (e.g., creative writing vs. factual QA). The router then acts like a wizard, directing queries to the expert. For example, if our model only has to run 10 out of 100 possible experts, it’s akin to having a 10B-parameter runtime cost instead of 100B.

This also improves our scalability, as adding more experts can boost the total capacity without increasing the cost for every example.

The router is the magic traffic controller. For each token:

1. Compute an affinity score for each expert—like, “Is this token about math? Code? General language?”

2. Select the top k experts (often k=1 or k=2).

3. Those ...