The Mechanics of Mixture of Experts: How LLMs Route Queries to Specialized Sub-Networks

When people hear that a frontier AI model has one trillion parameters, they usually assume the system uses all one trillion of those connections to generate every single word. It is a natural assumption—that is exactly how older, smaller neural networks worked.

But in reality, running a trillion-parameter dense model for every token would be computationally ruinous, requiring data centers the size of small cities just to answer a handful of prompts. Instead, modern frontier models use a technique called Mixture of Experts (MoE) to selectively activate only a tiny fraction of their "brain" at any given time.[4][5]

To understand how MoE works, it helps to look at the architecture it replaces. In a standard "dense" transformer model, every input token—a word or part of a word—passes through every single weight in the feed-forward neural network layers. If the model has 70 billion parameters, all 70 billion are multiplied together for every step of the sequence. This brute-force approach scales predictably, but it hits a hard physical limit: eventually, the electricity and silicon required to do the math outstrip the value of the output.[1][4]

Mixture of Experts solves this by introducing what engineers call "sparse activation." Instead of one massive feed-forward layer, an MoE model replaces that layer with a set of distinct, parallel sub-networks, which researchers call "experts." When a token moves through the model, it does not go to all of them. It is intercepted by a gating mechanism, or router, which mathematically evaluates the token and decides which specific experts are best equipped to process it.[1][4]

Mistral's highly successful Mixtral 8x7B model provides a perfect mechanical example of this routing in action. The model contains eight distinct expert networks. For every token it processes, the router selects exactly two of those eight experts. The token is sent to those two sub-networks, their outputs are combined, and the rest of the experts remain dormant for that specific computational step.[2]

This routing mechanism creates a massive divergence between a model's total knowledge capacity and its actual computing cost. Mixtral 8x7B contains 47 billion total parameters across all of its experts. However, because only two experts are active at any given time, the model only uses 13 billion active parameters per token. It possesses the reasoning capacity of a 47-billion-parameter giant, but it runs at the speed and compute cost of a 13-billion-parameter lightweight.[2][5]

Google's Switch Transformer research pushed this concept to its logical extreme. By configuring the router to send each token to just a single expert (top-1 routing) rather than two, researchers were able to scale a model to 1.6 trillion parameters. Despite its staggering size, the sparse activation meant the model maintained the computational footprint of a vastly smaller system, achieving a seven-fold increase in pre-training speed compared to dense models with the same computational budget.[3]

However, while MoE elegantly solves the compute bottleneck, it does not solve the memory bottleneck. This is the most common point of confusion for developers trying to run these models locally. Even though only 13 billion parameters are active during a Mixtral inference step, the router might need to call on any of the eight experts at a moment's notice. Therefore, all 47 billion parameters must be loaded into the GPU's memory (VRAM) simultaneously.[1][5]

This dynamic creates what hardware engineers call the "memory wall." MoE models are incredibly fast and cheap to run in terms of raw processing power (FLOPs), but they require massive amounts of expensive VRAM just to sit idle waiting for the router's instructions. To mitigate this, large-scale deployments use "expert parallelism," splitting the different expert networks across multiple physical GPUs so no single chip has to hold the entire model.[1]

Training an MoE model introduces its own unique mechanical hurdles, the most notorious being "routing collapse." If left to its own devices during early training, the router network will often discover that one or two experts are slightly better at predicting the next word than the others. It will then start sending every token to those "favorite" experts, starving the rest of the network of training data. To prevent this, engineers must introduce auxiliary loss functions—mathematical penalties that force the router to distribute tokens evenly across all available experts.[3][5]

What exactly are these experts learning? Despite the anthropomorphic name, experts do not neatly divide into human-understandable categories. You will not find one expert dedicated to "French," another to "Python code," and a third to "history." Instead, they specialize in complex, high-dimensional syntactic structures and token patterns that are often entirely opaque to human interpretation. A single line of code might be routed through four different experts depending on the punctuation and context.[1][4]

Ultimately, Mixture of Experts is the invisible engine powering the current era of generative AI. By decoupling a model's total parameter count from its active compute requirements, MoE allows the industry to continue scaling up intelligence without requiring an impossible scaling of electricity and silicon. It is the architectural trick that makes frontier AI economically viable.[4][5]