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Plain-language AI glossary

Term 66ModelsMeaning / context / connections

Models / Definition

Mixture of Experts (MoE)

A model architecture where multiple specialized sub-networks ("experts") are combined, with a gating mechanism that routes each input to the most relevant experts.

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MeaningThe one-sentence definition.

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ContextHow the idea works in practice.

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UsesWhere the concept becomes useful.

01 / Plain-language context

How Mixture of Experts (MoE) works.

A mixture-of-experts model contains multiple specialist subnetworks and a router that selects a subset for each token or input. Because only some experts are active at a time, the model can increase total capacity without using every parameter for every inference.

MoE systems introduce their own challenges, including routing quality, load balancing, communication overhead, memory use, and more complex training and serving. Providers do not always publish the architecture of closed models, so avoid treating speculation as a confirmed product fact. For most application teams, measured quality, latency, cost, and reliability matter more than the hidden routing design.

02 / Practical uses

Where it helps.

  1. 01Large-scale language model architectures
  2. 02Cost-efficient model scaling
  3. 03Multi-domain AI systems
  4. 04High-throughput inference services
  5. 05Research into model specialization
03 / Keep exploring
Transformer Architecture (Detailed)The complete technical architecture of the Transformer, including multi-head self-attention, positional encoding, feed-forward layers, and the encoder-decoder structure.Foundation ModelA large, general-purpose AI model trained on broad data that serves as a base for many downstream tasks through fine-tuning, prompting, or adaptation.Inference OptimizationTechniques to make AI model predictions faster, cheaper, and more efficient in production, including quantization, batching, caching, and model distillation.Neural NetworkA computational system inspired by the brain, composed of layers of interconnected nodes (neurons) that learn patterns from data through training.