Originally published on AI Tech Connect.
What you need to know about Llama 4's architecture Llama 4 is Meta's most significant open-weight release since the original Llama sparked an entire ecosystem. Both Maverick and Scout are built on a mixture-of-experts (MoE) architecture — a design choice that changes the economics of inference in a way that matters enormously for builders working outside hyperscaler budgets. In a standard dense transformer, every parameter participates in every forward pass. Inference cost therefore scales linearly with total parameter count. MoE breaks this relationship. The model is split into many specialised sub-networks called "experts". For each token, a learned router selects a small subset of experts to activate — in Llama 4's case, 17 billion parameters out of a much larger total weight. The rest…
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