What a mixture of experts is
A mixture of experts is an AI model design that divides the model into many specialized parts, called experts, and uses only the relevant ones for any given input. Instead of running the entire model for every task, a routing mechanism decides which experts are best suited and activates just those. This lets a model be very large and capable overall while only using a fraction of itself for each individual input.
The problem it addresses
Larger AI models tend to be more capable, but running a giant model in full for every single input is extremely expensive in computing power and energy. There is a tension between wanting big, capable models and keeping them efficient to run. Mixture of experts resolves this by making it possible to have a huge total model while only engaging the portion needed at any moment, cutting the cost per input.
How it works
The model contains multiple experts, each of which can become specialized in certain kinds of inputs or patterns. A routing component looks at each input and selects which experts should handle it, sending the input only to those. Their contributions are combined to produce the output. Because only a few experts activate per input rather than the whole model, the work done for each input stays manageable despite the model's large total size.
The benefits
The main benefit is efficiency at scale. A mixture of experts can have an enormous number of total parameters, giving it great capacity and capability, while the amount of computation per input stays far lower than running a dense model of the same total size. This allows building very large, powerful models that remain practical to run, an attractive combination that has made the approach increasingly popular.
Why it matters
Mixture of experts matters because it offers a path to ever more capable AI without the cost growing in lockstep with size. By activating only the needed parts, it makes very large models feasible to run, which is why the design features in many advanced modern models. Understanding it reveals a key technique behind scaling AI efficiently, letting models grow in capability while staying practical to operate.
The bottom line
A mixture of experts splits an AI model into specialized experts and uses only the relevant ones for each input, so a very large model stays efficient by activating just a fraction of itself at a time. This lets models grow in capability without cost rising in step with size. Popular in advanced modern AI, understanding it explains a key technique for scaling models efficiently.
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See also: What is a large language model (LLM)?, What is a transformer model? Explained.
Sources
Published date reflects the original event date (2026-10-23). This article is original Skillo editorial written from the sources above; facts were verified in September 2026.
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