📌 Day 17: 21 Days of Building a Small Language Model: Mixture of Experts📌

MoE (Mixture of Experts) is often pitched as the silver bullet for scaling models.

Instead of activating all parameters for every token, MoE routes tokens to specialized experts.

This sparse activation lets you scale capacity 8×, 64×, or more, while keeping per-token compute close to dense models.

But MoE comes with real hidden costs:

✅ Training becomes significantly more complex

✅ Inference latency turns unpredictable

✅ Memory overhead explodes

✅ Expert collapse is a constant risk

For models under ~1B parameters, dense architectures are:

✅ Simpler to train

✅ More stable

✅ Easier to debug

MoE is powerful, but it’s a tool for a specific problem:

👉 scaling to massive model sizes where dense models are no longer feasible.

If you’re building small or medium models, start dense.

The complexity and overhead of MoE usually aren’t worth it.

🔗 Blog link: https://www.linkedin.com/pulse/day-17-21-days-building-small-language-model-mixture-experts-lakhera-3lqdc

I’ve covered all the concepts here at a high level to keep things simple. For a deeper exploration of these topics, feel free to check out my book "Building A Small Language Model from Scratch: A Practical Guide."

✅ Gumroad: https://plakhera.gumroad.com/l/BuildingASmallLanguageModelfromScratch

✅ Amazon: https://www.amazon.com/dp/B0G64SQ4F8/

✅ Leanpub: https://leanpub.com/buildingasmalllanguagemodelfromscratch/