--- language: en title: "Mixture of Experts" description: "Mixture of Experts uses routing to activate parts of a network. Learn why total and active parameters help evaluate different models." canonical: "https://tropk.ai/en/resources/glossary/mixture-of-experts" last_modified: "2026-10-12T16:13:21.709Z" --- # Mixture of Experts Reviewed on 2026-10-06. ## What it means Mixture of Experts, or a mixture of experts, is an architecture that routes inputs to subsets of a neural network called experts. A routing network selects which parts process each token or example. In sparse architectures, only some of the parameters are activated per input, allowing total capacity to increase without running all the experts for every processing task. ## Why it matters for marketing For marketing, this concept helps interpret infrastructure proposals and comparisons between models used in assistants. A high total parameter count alone does not indicate how much work each request requires. Buyers need to consider task quality, available resources, and response time rather than treating the advertised size as proof of superiority. ## Example in practice Imagine an agency comparing two models for organizing briefs: a dense model and an MoE model. The second model’s specifications distinguish between total and active parameters per token, while the first lists a single count. The agency avoids comparing these figures as equivalent. It requests a test using the same briefs and chooses based on classification errors and latency. ## How to apply or verify Read the architecture documentation to confirm whether routing is used and which counts are reported. The technical team should check memory requirements and the cost of serving the model under the expected workload. Compare responses using the same protocol, keeping parameter counts, observed quality, and operational performance metrics separate. ## Important distinctions Experts are mathematical components, not human professionals, and are not necessarily modules labeled by business topics. MoE also does not mean a team of agents conversing or choosing between independent APIs. The Switch [Transformer](/en/resources/glossary/transformer) study describes gains under specific conditions, as well as communication and training challenges; the architecture alone does not guarantee lower costs or better responses in every application. ## Sources and references - [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity]() — Fedus, Zoph e Shazeer / arXiv - [Machine Learning Glossary: mixture of experts]() — Google for Developers ## Continue a leitura - [Inference]() - [Token]() - [Transformer]() ## About the author [Vinicius Pacheco]() Responsible for Tropk’s technical content, focusing on crawling, indexing, structured data, and agent access to content. --- [All pages in Markdown]()