MoE
Mixture of Experts
In one line
MoE (Mixture of Experts) is an LLM architecture that activates only a subset of many smaller 'expert' networks per token — letting teams ship bigger models at roughly the same compute cost.
Going deeper
MoE puts dozens or hundreds of smaller 'expert' networks inside one model and uses a router to activate only a few of them per token. Total parameter count is enormous, but compute per call stays modest — so you get something close to a giant model's quality at a small model's price. Mixtral, DeepSeek and some GPT-4-family variants are well-known examples.
Marketers will not configure MoE themselves, but its second-order effect is real. Cheaper inference is pushing more products to embed LLMs, which means more AI surfaces where your brand may or may not show up. The pace of GEO surface expansion is partly an MoE story.
Worth knowing the trade-offs: MoE answers can be uneven if the router picks badly, and memory requirements are higher than the parameter math suggests. The 'same price, bigger model' headline has fine print.
Sources
Related terms
LLM
A large language model (LLM) is a neural network trained on massive text corpora to understand and generate human language — the engine behind ChatGPT, Claude, Gemini and similar products.
LLMTransformer
The Transformer is the neural network architecture behind almost every modern LLM, using self-attention to weigh relationships between all tokens in a sequence in parallel.
LLMOpen-weight Model
An open-weight model is an LLM whose weights are publicly released so anyone can download and run it on their own infrastructure — Llama, Mistral and Qwen are the best-known examples.
LLMQuantization
Quantization compresses model weights to lower precision (say, 16-bit down to 4-bit) so the same model fits on smaller GPUs and runs more cheaply.
LLMModel Routing
Model routing dispatches each query to the most suitable model based on difficulty or category — the de-facto pattern for balancing cost, accuracy and latency in production AI.
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