What Is a Mixture of Experts (MoE) Model? How MoE Makes Large AI Models More Efficient

Mixture of Experts (MoE) is a neural network architecture that divides parts of a model into multiple specialized modules, known as experts, and dynamically activates only a small subset of them for each input. The idea dates back to a 1991 paper on local experts. Its central idea is to increase a model’s overall parameter capacity without requiring every parameter to participate in every computation.

For example, an MoE model may contain many expert networks, while a Router selects only one or two of them to process a particular token. A different token may be sent to a different combination of experts. Importantly, these experts are not necessarily predefined as "math," "coding," or "language" specialists. Their specialization typically emerges during training as they learn different patterns in the data.

For AI developers, machine learning engineers, researchers, and other technical readers working with Transformers and large language models, this is the key to understanding how MoE differs from a traditional dense model: only part of the network runs for each token. That design has made MoE an important architectural approach for scaling modern AI systems, because it can raise model capacity while keeping inference and computation more efficient. This introduction explains how expert routing works, how MoE connects to LLMs, where it differs from dense architectures, and the main benefits, tradeoffs, and inference considerations.

What Is a Mixture of Experts (MoE) Model?

Mixture of Experts is a machine learning technique and architecture that uses multiple learnable expert networks and dynamically selects which experts should process a given input.

The basic idea is straightforward: instead of making every input pass through exactly the same large network, the model provides multiple possible computational paths and selects a small number of them based on the input.

In a Transformer, MoE usually does not mean dividing the entire model into several independent models. Instead, some standard Feed-Forward Network (FFN) layers or a dense feed forward network can be replaced by multiple expert FFNs. Other components, such as attention layers, may still be shared across all tokens.

A typical MoE layer therefore contains three important elements:

The key characteristic of this architecture is sparse activation. Although the model may contain a very large number of parameters, each token activates only a fraction of the available experts, using only a subset of model parameters rather than all the parameters.

How Does a Mixture of Experts Model Work?

The MoE process can be understood by following a token, or more generally other input data, as it moves through a Transformer.

First, text is divided into tokens and transformed into numerical representations. After passing through embeddings and earlier Transformer computations, each token has a hidden representation that contains contextual information.

When this representation reaches an MoE layer, the Router acts as a router network and calculates routing scores for the available experts. Based on these scores, a routing strategy such as Top-1 or Top-2 selects a small number of experts to process the token.

Suppose an MoE layer contains eight experts. For one token, the Router might select Expert 2 and Expert 6. Only those selected experts perform the relevant expert computation for that token instead of all eight experts being activated. In practice, only the experts chosen by that routing step run for the token. Another token might be routed to Expert 1 and Expert 4.

Once the selected experts finish processing the token, their outputs are combined according to the routing weights and passed to the next part of the Transformer.

The process can be summarized as:

Token → Hidden Representation → Router → Select Top-k Experts → Expert Computation → Combine Outputs → Next Transformer Layer

This ability to dynamically select computational paths is one of the defining differences between MoE and conventional dense architectures, because it lets the system scale without using all model parameters at every step.

Are MoE Experts Really Specialists in Different Subjects?

The term "expert" can be misleading. It may sound as though an MoE model contains a math expert, a coding expert, a translation expert, and other clearly defined specialists.

In practice, the distinction is usually much less explicit.

An expert is a learnable neural network module inside the larger model. Developers generally do not manually assign each expert a specific profession or knowledge domain. During training, different experts may become more responsive to certain tokens, linguistic structures, data patterns, or internal features, but their specialization may not map neatly to human-readable categories.

Routing also commonly happens at the token level, rather than assigning an entire Prompt to one expert. Different tokens within the same sentence can therefore be routed to different experts.

A more accurate way to understand an MoE expert is as one of several computational modules that the model can dynamically activate, rather than as a small independent AI with a clearly defined area of expertise.

How Does the Router Select Experts?

The Router is one of the most important components in an MoE architecture because it determines which computational path each token will take.

When a token’s hidden representation enters an MoE layer, the Router calculates scores that indicate how strongly the token should be associated with different experts. A Top-k routing strategy then selects the experts with the highest scores.

For example, an eight-expert layer might produce an illustrative distribution like this:

With Top-2 Routing, Expert 2 and Expert 6 would be selected. Before that selection, the routing scores act like a probability distribution over the available experts. These values are only an example used to illustrate the mechanism and do not represent the output of a particular MoE model.

The Router itself is learned during model training. Ideally, it learns to distribute tokens across useful computational paths. However, routing introduces another challenge: if too many tokens are repeatedly sent to the same few experts, those experts can become overloaded while others remain underused.

For this reason, MoE training often includes additional mechanisms designed to improve load balancing across experts. In a traditional MoE setup, a softmax router picks a few experts for each token through Top-k selection. Some systems also use noisy Top-k gating, which injects noise to improve load balancing. Another stability aid is router z loss, which penalizes overly large routing logits during training.

What Is the Difference Between MoE and Dense Models?

The main difference between an MoE model and traditional dense models is how much of the model participates in computation for each token.

In a typical Dense Transformer, each token follows essentially the same major network path, meaning the parameters in those layers participate in every relevant forward pass. Increasing the size of a Dense model therefore generally increases the amount of computation required for each token.

MoE introduces multiple experts and activates only a subset of them. This allows the model to increase its total parameter capacity without requiring every expert parameter to participate in every token computation.

This is why total parameter count alone can be misleading when comparing Dense and MoE models. In practice, sparse models often need different optimization strategies because they can be more prone to overfitting than dense models.

For an MoE model, another important concept is active parameters: the parameters that actually participate in the relevant computation for a token. A model can have a very large total parameter count while activating only a much smaller subset during each expert-routing step.

Why Do Large AI Models Use MoE?

One of the main advantages of MoE is that it partially separates model capacity from per-token computation, making it a practical method for scaling giant models more efficiently.

With a conventional Dense architecture, increasing parameter count generally means increasing the amount of computation required when processing each token. A larger model may offer greater capacity, but it also tends to require more compute and memory resources.

MoE provides another scaling approach. This pattern has been used for giant models and even trillion parameter models or multi trillion parameter models. Developers can add more experts and increase the model’s overall parameter capacity while allowing the Router to activate only a small subset of those experts for each token.

This makes it possible to scale parameter capacity without making per-token expert computation grow at exactly the same rate as the model’s total parameter count. That sparse scaling can be especially useful under a fixed compute budget.

However, this does not mean MoE automatically makes AI inexpensive. Additional expert weights still need to be stored, and expert routing introduces new challenges involving communication, memory, load balancing, and distributed infrastructure.

The advantage of MoE is therefore better understood as a different trade-off between model capacity and computational efficiency, rather than a simple way to reduce every type of AI cost. It can also reduce carbon footprint by up to an order of magnitude.

Does MoE Make AI Inference Faster and Cheaper?

Sparse activation can reduce how many expert parameters need to participate in processing each token, but this does not mean every MoE model will automatically run faster or cost less than every Dense model.

From a computational perspective, MoE can provide greater parameter capacity within a given active-compute budget because only selected experts process each token. This is the fundamental source of its efficiency advantage, although the actual gains still depend on available computational resources rather than sparse activation alone in theory.

Real-world inference performance, however, depends on much more than active parameter count. Hardware configuration, memory capacity, batch size, routing efficiency, model implementation, communication between accelerators, and modern hardware can all affect performance.

Even experts that are not currently active still have weights that need to be stored and managed. In a distributed MoE deployment, experts may also be placed on different GPUs. Routing a token to an expert located on another device can introduce additional communication overhead.

MoE should therefore be understood as a way to improve the scaling efficiency of neural networks through sparse computation, rather than a guarantee of lower latency or lower inference cost in every deployment.

What Challenges Do MoE Models Face?

MoE improves model capacity but also introduces performance challenges that are less prominent in conventional Dense architectures, especially when sparse MoE layers are added. One of the most important is load balancing.

If the Router consistently sends a disproportionate number of tokens to a small group of experts, those experts can become overloaded while others remain underutilized. This reduces the benefits of having multiple experts and can create computational bottlenecks.

MoE training therefore commonly includes auxiliary objectives or other techniques designed to encourage more effective expert utilization. Systems may also place limits on how many tokens an expert can process within a particular batch or routing step, with capacity factor settings and low capacity factors serving as practical controls for expert load. MoEs also require careful fine tuning to avoid training instabilities.

Distributed computation creates another challenge. In large MoE models, experts may be spread across multiple GPUs or computing nodes. Dynamic routing requires the system to send token representations to the appropriate experts and then return their outputs to the main computation flow.

As a result, MoE requires careful design around routing algorithms, expert capacity, training stability, memory management, and accelerator communication. Building an MoE model involves both neural network architecture and AI infrastructure engineering, and transferable sparse expert models depend on stable routing, balanced expert models, and well-chosen moe parameters.

How Are MoE, Transformers, LLMs, and AI Inference Related?

MoE is not an alternative to Transformer models. Instead, it is an architectural technique that can be incorporated into a Transformer model.

A standard Transformer layer typically includes an Attention mechanism and a Feed-Forward Network. In an MoE Transformer, some conventional FFNs can be replaced with multiple expert FFNs, with a Router determining which experts process each token.

The relationship can be summarized as:

Transformer provides the underlying architecture → MoE changes how some network layers allocate computation → LLMs use these structures to learn language patterns → AI Inference executes the computations needed to process and generate tokens.

MoE architectures can effectively handle a variety of AI tasks beyond language, including computer vision and other computer vision tasks.

When a user accesses an MoE-based LLM through an AI API, they generally do not manually choose the internal expert. Expert routing happens inside the model. At the application level, the user still sends a Prompt and receives a model output.

This also highlights an important distinction between MoE routing and Model Routing. MoE routing selects experts inside a single model, often at the token level. Model Routing operates at a higher infrastructure layer and typically selects among separate AI models based on the request, cost, latency, capabilities, or other requirements.

The two concepts both use the word "routing," but they solve different problems. Switch Transformers are a prominent MoE example, and reported results showed a 4x pre-train speed-up over T5-XXL.

Summary

Mixture of Experts (MoE) is a neural network architecture used in deep learning systems that can be viewed as a deep neural network design pattern with multiple specialized models and a Router to enable sparse computation. Instead of activating every expert for every token, the model dynamically selects a small subset, allowing total parameter capacity to grow without requiring all expert parameters to participate in every computation.

Within a Transformer, MoE commonly replaces some standard Feed-Forward Networks with multiple expert FFNs. A Router evaluates each token’s hidden representation, selects the Top-k experts, and under Top-2 routing may activate both the experts chosen for a token and combine their outputs before the information continues through the model.

However, MoE does not automatically mean faster or cheaper inference. Expert parameters still need to be stored, while dynamic routing introduces challenges involving load balancing, memory, accelerator communication, and distributed deployment.

For this reason, understanding MoE requires looking beyond a model’s total parameter count. Concepts such as active parameters, sparse activation, expert routing, and load balancing provide a clearer picture of how MoE models scale and why this architecture has become important in modern large AI systems.

FAQ

Are the experts in an MoE model separate large language models?

Usually not. Experts are generally neural network modules within the same model, such as separate Feed-Forward Networks, while sharing other parts of the Transformer architecture and the broader training process.

Does an MoE model use all of its parameters for every inference request?

No. MoE uses sparse activation, so each token typically activates only a subset of the available experts. The number of active parameters can therefore be substantially smaller than the model’s total parameter count.

What is the difference between Top-1 and Top-2 Routing?

Top-1 Routing selects the highest-scoring expert for each token, while Top-2 Routing selects two experts and combines their outputs; for example, Switch Transformers use a Top-1 routing style, while other MoE systems may use Top-2. The approaches involve different trade-offs in computation, routing behavior, and model performance. After pretraining, instruction tuning can further change performance for either routing style.

Is MoE routing the same as Model Routing?

No. MoE routing selects expert modules within a single model, while Model Routing usually selects among separate AI models at the application or infrastructure level.

Why do MoE models need load balancing?

Without load balancing, a Router may send too many tokens to a small number of experts, creating bottlenecks while leaving other experts underused. This matters partly because each token is handled by only a few experts. Load-balancing mechanisms help distribute computation more effectively across the available experts. In some designs, only a quarter of the layers are expert layers, which can limit how broadly updates affect the model during adaptation.