What is Mixture of Experts (MoE)?

Mixture of Experts (MoE) is a model architecture technique that speeds up AI inference by routing tasks to the most capable part of the model. 

MoE models are specially trained to answer specific subcategories quickly and accurately. 

Why you should care about inference 

Think of it this way: If you were a student with a question about human anatomy, would you knock on every professor’s door until you got your answer? Or would you go to your biology professor first? You’d likely go straight to your biology professor, the best match in your “mixture of experts.” 

Why? Because you want the correct answer as quickly as possible.

Even though all your professors are knowledgeable in their own subjects, you knew your biology professor would have the answer when it came to human anatomy. That’s why you asked them, instead of taking a detour through the English department. 

Mixture of Experts uses the same logic. 

Read a blog post about scaling intelligence with MoE 

For inference to be successful, AI models need to do a lot of calculations in a short period of time. As models get bigger, they become more complex and inference slows down. Factors like model size, high user volume, and latency can all limit performance. 

To overcome these challenges, Mixture of Experts creates a neural network that supports faster inference at scale. 

How does MoE use deep learning? 

Deep learning is an AI technique that teaches computers to process data and learn through observation, imitating the way humans gain knowledge.

There are 2 defining characteristics that support model function: 

• Transfer learning is when a model applies information about 1 situation to another and builds upon its internal knowledge. Many foundation models have hundreds of neural layers that are pretrained with deep learning techniques. This is how models discover relationships and patterns within a dataset.
• Scale refers to hardware—specifically graphics processing units (GPUs)—that allows the model to perform multiple computations simultaneously. 

MoE integrates deep learning training and transfer learning to identify patterns and subcategories within prompts. MoE models can then quickly identify the best “expert” to answer the input. MoE uses GPUs to scale and accelerate the prompt-to-answer pipeline. 

Learn more about foundation models 

How does MoE use neural networks? 

Neural networks form the underlying architecture of deep learning. They’re made up of many layers of neurons that interpret data. 

Traditionally, each layer interprets incoming data and sends it to the next layer, and so forth, until it reaches a neuron that can answer the prompt. These typically dense neural networks are called feed-forward networks (FFNs). 

FFNs send data in 1 direction, through all of its parts: input layers, hidden layers, and output layers. As data flows from the input layers to the output layers, hidden layers learn the patterns and trends of each input to deliver a final result. 

Unlike FFNs, MoEs can take multiple paths to deliver an output. When MoEs identify experts, it shortens the path to a final result and expands model capacity. This is how models learn new information and identify patterns without using more memory, compute, or time. 

To block out the noise of other computations happening at the same time, MoE introduces sparsity.

How does MoE use sparsity?

Sparsity is a technique that helps neural networks save memory by using fewer weights. 

Weights are calculations that tell a model what action to take. Each weight is scored based on its ability to answer each prompt. This allows the input to be mapped to the correct expert. But, not every weight is necessary for every prompt. Sparsity identifies the necessary weights and ignores the weights that aren’t critical. 

In technical terms, this means unnecessary weights are set to 0. When the model sees 0, it knows to skip those calculations (because anything multiplied by 0 equals 0). This means experts can focus only on the weights that matter.

When unnecessary weights are hidden, the model has more memory and can work faster. The tricky part is finding the sweet spot between increasing speed and decreasing accuracy or performance.

Find more ways to optimize inference 

A majority of foundation models use a type of neural network known as transformers. They help models capture contextual relationships and dependencies in data sequences. Developers often replace dense architectures with MoEs to make the model more efficient.

MoE is made up of 2 main parts: Sparse neural network layers and a gate network. 

• MoE sparse models in a neural network have fewer connections than dense layers. 

  To enforce sparsity, these models compute only necessary calculations rather than all of them. With fewer connections, the neural network saves more memory and can work faster. 

  A dense layer operates similarly to a web browser with dozens of open windows. The internet begins to slow down because it’s processing so many different signals in tabs that remain open, but untouched. This takes up a lot of memory and causes the 1 tab you actually need to move slowly. 

  Sparse layers disregard the unnecessary connections in the neural network so the ones you need can move as quickly as possible. In our browser analogy, sparse layers understand which open tabs to ignore and which tab needs to run smoothly.

   

• MoE gate networks or routers analyze each prompt and route it to the most capable expert. This allows MoEs to take multiple paths to reach their result.

  Using pretrained parameters, the gate network scores each expert and selects the best ones for each request. This selection creates sparsity—only the chosen experts are activated, while the rest are skipped. This allows the model to focus compute on what matters most. 

  Once the experts get their scores, the gate network delegates the prompts accordingly.

  For example, the gate network receives the input to write an original fairytale. The router identifies an expert trained in creative writing based on its high score in this subject. Other experts trained in medicine, marketing, and engineering receive low scores. The gate network selects and activates the most relevant expert and skips the others. Because of this training, the gate network knows to route the prompt to the creative writing expert for the best possible output. 

MoE architecture allows multiple specialized models to work together. So oftentimes, the router identifies more than 1 expert that can answer the prompt quickly. After the experts have completed their tasks, the gating network collects the results and combines them for a final, cohesive answer.

Learn more about AI infrastructure 

Mixture of Experts helps models run faster with fewer resources, providing several advantages:

• Speed. Just like the student who saved a lot of time and effort by going straight to their biology professor, an MoE model saves significant time and resources by ignoring unnecessary data and going directly to the expert. This means MoE models outperform dense models that process every dataset for every prompt.
• Specialization. As MoEs process more prompts, they get better at recognizing patterns and data in their specific topics. This makes MoE models more accurate than dense models that see each prompt and try to master every topic at once.
• Scale. MoEs activate only the necessary weights for each task, so they can handle high computational demand. Unlike dense models, MoEs don’t activate millions of parameters with every inference. This way, you can scale your infrastructure without a massive investment in resources. 

Fine-tuning MoEs

Traditional fine-tuning is challenging because updating billions of parameters can lead to overfitting, or when a model memorizes specific data rather than learning general patterns. However, MoEs have a unique challenge: routing instability.

MoE models rely on a gate network to send information to specialized experts. But if the gate sends new data to the wrong experts or if certain experts are overused, the model can experience: 

• Expert collapse: when the model loses its specialized diversity.
• Catastrophic forgetting: when experts lose their original specialized knowledge. 

Learning new data without losing or disrupting the current knowledge base can be a major technical hurdle.

Load balancing MoEs 

In an MoE model, experts primarily learn from tokens the gate network sends. This creates a "rich-get-richer" cycle called expert imbalance: if the gate identifies a successful expert early on, it becomes slightly smarter, making the gate more likely to choose it again. Without intervention, a few experts become overburdened while the rest remain undertrained or underutilized.

However, most modern MoE implementations include load-balancing losses and routing strategies to prevent this.

MoE memory requirements

MoE models are efficient, but they require a lot of storage. 

MoEs use a large number of parameters to train each model on its specific topic. Despite using sparsity, MoE still needs hardware for all the experts in its network. Those experts aren’t always in use, but they do take up space. 

High memory requirements typically lead to increased hardware needs and higher costs. 

Training MoEs is more complex than training a standard dense model. Success depends on the gate network and the experts learning to coordinate in sync. If these 2 components don’t learn to work together, the architecture can’t route tasks or process data effectively. 

Input routing and expert selection

Input routing is how the gate network makes real-time decisions to accurately match each prompt with an expert. 

The gate network is trained to identify top qualifying experts referred to as “top-k experts.” (The “k” is a placeholder for the number of top scoring experts that should be activated to answer each prompt.) Since MoEs use sparsity, this number is low, typically 1 or 2. All other experts are set to 0 and ignored. 

Expert training

To work well, experts need to be trained equally. The catch is they can only learn from the prompts the gate network sends. 

As the network identifies which experts are trained in certain topics, it learns to route those prompts accordingly. If an expert consistently answers scientific questions correctly, the gate will learn to send it more questions on biology, chemistry, and physics. This helps those experts build deep, niche knowledge and recognize complex patterns over time.

How to avoid a lazy gate network 

If an expert gets really good at answering different types of prompts, the gate may begin to send it a disproportionate number of inputs. This leads to overfitting or unbalanced loads. 

To prevent this, developers use a load-balancing loss, or auxiliary penalty. It’s a machine learning technique that teaches the gate rules around fairness and distribution. When it's penalized for choosing 1 expert too often, it will learn to try other experts. Over time, the gate network learns to balance the workload and distribute prompts across its experts. 

This reinforces the idea that all experts specialize in something unique and continue to collect data and patterns in their own niche topics. 

Get the basics on RAG vs. fine-tuning 

As models and datasets get bigger, they require more GPUs for storage. Expert parallelism scales Mixture of Expert models and architectures across hardware to use resources more efficiently. 

First, it’s helpful to understand data parallelism. This AI scaling strategy divides a large dataset into categories and distributes each piece of data to a separate processor or GPU. The GPUs work alongside each other at the same time and deliver a consistent, cohesive output. Then, the gate network combines outputs to deliver a final result. 

Expert parallelism applies this strategy by distributing experts across multiple GPUs. When a request comes in, the gate routes tokens to the devices that host the most relevant experts, even if they live on different machines. Each expert processes prompts at the same time and then combines their results to provide an answer. By splitting up the inference processing, models can process inputs and use compute more efficiently at scale. 

This is different from the MoE model architecture, because it’s using experts across hardware at scale. It’s not distributing model inputs—it’s distributing experts across many different GPUs. 

MoE is like doing a group project. The teacher gives your group an assignment, and your team delegates each task to a different team member based on their skills. Once everyone does their part of the assignment, you’re ready to present a cohesive project.

Expert parallelism is like an entire school district working together to raise money. Each school works in different locations at the same time for the same cause. When more than 1 school participates in fundraising, they’re likely to raise more money in less time. 

Expert parallelism can experience the same challenge as MoEs: load balancing. When the gate routes too many tokens to experts on the same GPU, utilization can become uneven and a potential bottleneck. It’s important to monitor GPUs to make sure 1 isn’t working harder than the others. 

What is distributed inference? 

AI engineers, model developers, and cloud service providers use MoEs. They’re popular among machine learning and enterprise AI teams. 

MoE is typically helpful when:

• You want to increase model capacity without significantly increasing compute per request.
• The problem benefits from specialization, where different parts of the model can learn different patterns.
• Your large-scale, high-throughput scenarios require more compute or multiple machines.
• You need to make efficient use of a fixed compute budget during training or inference.

MoE can excel in topics in the following scenarios: 

• Natural language processing (NLP): MoE can support NLP with prompts like summarizing long documents, indicating positive or negative sentiment in comments, and generating insight for automated virtual assistants and chatbots. 

  For example, a chatbot assistant may use an MoE architecture to direct questions in another language to an expert who’s been trained in specific human languages. 

• Computer vision: MoEs can use deep learning techniques to comprehend images the same way humans do. This includes things like facial recognition and image classification. 

  For example, MoEs can help AI-assisted medical imaging identify different categories of images—like X-rays, MRIs, and CT scans. Different experts may specialize in identifying abnormalities like fractures or tumors. 

• Recommendation systems: MoE can predict user preferences by analyzing past behavior and context. 

  For example, streaming platforms like Netflix and Spotify analyze your behavior and predict preferences. When you log in, the service immediately surfaces the content you’re most likely to enjoy. MoEs excel in identifying these trends faster and more accurately.

Remember, dense models can handle all of these use cases, too. But they may not move as quickly or be as highly trained on specialized topics. The benefit of MoEs is that they can help quickly and accurately. 

How to apply AI across the enterprise 

Mixture of Experts is a popular strategy for most open source models. More than 60% of open source AI models released in 2025 adopted MoE,¹ signaling the industry’s interest and understanding of its value.

Some open source MoEs include: 

• Mixtral 8x7B
• OLMoE
• DBRX
• OpenMoE 

MoE has proven building bigger models to handle more compute isn’t always the best strategy. MoE open source models are reaching higher intelligence levels faster because of their ability to learn specialized topics faster than dense models. 

Read about small language models 

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The Red Hat AI portfolio includes Red Hat AI Inference Server, an inference stack that provides the operational control to run any model on any accelerator across the hybrid cloud. Learn how Red Hat AI can help enterprises get fast, efficient, and cost-effective inference at scale. 

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¹Koparkar, Shruti. “Mixture of Experts Powers the Most Intelligent Frontier AI Models, Runs 10x Faster to Deliver 1/10 the Token Cost on NVIDIA Blackwell NVL72.” NVIDIA blog, 3 Dec. 2025.