Definition
Mixture of
experts (MoE) is a machine learning technique that splits an artificial
intelligence (AI) model into different sub-networks or experts, each focusing
on different aspects of the input data, working together to accomplish a task
[1].
That is, it avoids relying on one big model to solve all problems. Instead, it employs multiple smaller models, with each smaller model specializing at solving a specific type of problem. A gating mechanism, which is the decision maker, selects which smaller model to use for each task, optimizing the entire system. Simply put, it is division of labor, with each expert handling specific tasks for best possible outcome.
Let’s use a Federal
Government College as an example, with different teachers each having unique
expertise. A mixture of experts (MoE) model behaves based on this principle, by
dividing student learning into smaller, specialized networks known as
"experts."
Each expert handles
specific aspect of the problem, allowing the model to teach students more
efficiently and accurately. It's similar to having a mathematics teacher for
algebra and calculus, a chemistry teacher for laboratory experiments, and an
English teacher for literature and composition. Each expert handles what they
do best.
By teaching their specialized subjects, these educators prepare students for WAEC and JAMB more effectively when compared to a single teacher trying to teach all subjects from Igbo language to Christian Religious Knowledge.
Origin
The “Adaptive
Mixtures of Local Experts” by Robert Jacobs, along with the Geoffrey Hinton, “Godfather
of AI” and colleagues, gave birth to the idea of the Mixture of Experts in the
90s, just before the era of Deep Learning. They introduced the idea of splitting
the neural network into many specialized “experts” managed by a gating network.
With the rise of
Deep Learning, the MoE re-emerged. In 2017, Noam Shazeer and colleagues
(including Geoffrey Hinton once again) came up with the Sparsely-Gated
Mixture-of-Experts Layer for recurrent neural language models.
The
Sparsely-Gated Mixture-of-Experts Layer is made up of multiple experts
(feed-forward networks) and a trainable gating network that determines the which
experts that handle each input. The gating mechanism enables conditional
computation, directing processing to the parts of the network (experts) that
are most suited to each part of the input text.
Such an MoE layer can be integrated into LLMs, replacing the feed-forward layer in the Transformer block. Its key components are the experts, the gating mechanism, and the load balancing [3].
Context and Usage
The use and
applications of MoE cuts across different domains such as the following:
- Object Detection: Experts can concentrate on identifying particular objects or types of objects such as cars, people, animals, in an image.
- Efficient Feature Extraction: MoE can employed to properly process large images through assigning different experts to different parts or scales of the image.
- Fraud Detection: Each expert could specialize in recognizing particular types of fraudulent transaction such as credit card fraud, account takeover based on different features of the transaction data.
- Portfolio Management: In algorithmic trading, experts can specialize in various market conditions or asset types, enabling improved decision-making.
- Disease Diagnosis: MoE can be used to model different disease types, where each expert specializes in a specific condition or set of symptoms.
- Drug Discovery: In bioinformatics, MoE can be used to predict molecular interactions or drug efficacy by activating experts trained on different biological processes or chemical properties.
- Manipulation Tasks: MoE enables robots employed in assembly or manipulation to properly handle various tools or objects, with each expert specializing in a particular manipulation skill.
- Sounds and Languages: Experts could focus on different speakers or types of speech or sounds such as formal vs. informal speech, male vs. female voices, noise vs. clear speech to improve recognition accuracy. Furthermore, MoE can assist to create multilingual models that assign specific experts to different languages or dialects [4].
Why it Matters
The Mixture of
Experts (MoE) is a machine learning approach that divides a large model into
smaller, specialized subnetworks called “experts.” Neural networks, particularly
ones used in deep learning, can get really big. Such as hundreds of billions of
parameters big. Running these models, especially during inference, can be a
massive computational burden. With the Mixture of Experts, you make them more
efficient and at the same time maintain good performance.
Each expert concentrate
on a specific subset of the input data. Rather than using the entire network
for every task, only the relevant experts are activated. This selective
activation reduces the computational load, making the model more efficient.
Related AI Models and Architectures
- Latent Space: Abstract mathematical space where AI models represent data in compressed, meaningful dimensions
- Model: Mathematical representation that learns patterns from data to make predictions or decisions
- Neural Network: Computing system inspired by biological neural networks that learns patterns from data
- Neural Radiance Fields (NeRF): AI technique for creating photorealistic 3D scenes from 2D images
- RoBERTa: Robustly Optimized BERT Pretraining Approach, an improved transformer language model
In Practice
DBRX is a good
example of a real-life case study of MoE in practice. Databricks is the
developer of DBRX, which is a transformer-based decoder-only large language
model (LLM) trained on next-token prediction. Utilizing a fine-grained
mixture-of-experts (MoE) architecture with 132B total parameters of which 36B
parameters are active on any input, it is good in use cases that has to do with
code generation, complex language understanding, mathematical reasoning, and
programming tasks, with strong performances in situations demanding high
accuracy and efficiency, such as generating code snippets, solving mathematical
problems, and providing detailed explanations in response to complex prompt [5].
References
- Bergmann, D. (2025). What is mixture of experts?
- Pandit, B. (2024). What Is Mixture of Experts (MoE)? How It Works, Use Cases & More
- Kirakosyan, N. (2025). Mixture of Experts LLMs: Key Concepts Explained.
- Iguazio. (2025). What is Mixture of Experts?
- Dutta, N. (2025). What is Mixture of Experts?