Why Bigger GPT Models Don’t Use All Their Parameters
Автор: ML Guy
Загружено: 2026-03-01
Просмотров: 60
Описание:
What if a language model didn’t need to use all of its parameters for every token?
Early Transformers activate everything at once — every layer, every neuron, every parameter. It works… but it doesn’t scale forever.
In this video, we break down Mixture of Experts (MoE), the architectural breakthrough that allows modern models to scale to massive parameter counts without increasing computation per token. You’ll learn how sparse activation works, how expert routing is trained, and why MoE models can reach trillion-parameter scale while remaining computationally efficient.
We cover:
Why dense Transformers become inefficient at extreme scale
How expert layers replace standard feed-forward networks
The role of the routing network (gating mechanism)
Top-k expert selection and sparse activation
Load-balancing losses and avoiding expert collapse
Why MoE increases capacity without increasing compute
Real-world examples like Switch Transformers and modern large-scale models
Mixture of Experts isn’t just about making models bigger.
It’s about making them selective.
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