Imagine building an extremely complex business solution. Instead of relying on one giant brain for everything, what if you had a team of specialized minds—each an expert in a different area? That’s the idea behind Mixture of Experts (MoE).
MoE is a breakthrough architecture that makes large language models (LLMs) more efficient, scalable, and intelligent—without requiring massive computational power.
Each "expert" in an MoE model is a miniature neural network that is trained to handle one kind of input. A complete model can have dozens—or even hundreds—of specialists, each of which is trained to perform a specialized task such as language comprehension, programming, logic, or summarization.
MoE models don't give tasks randomly. Rather, they use a gating network—a type of smart router—to determine which experts are appropriate for a specific input. As an example, if the input is code that needs to be debugged, the model will engage a "programming expert" and a "logic expert."
Rather than activating all the experts (which is costly in terms of computation), a small subset is activated—typically 2 to 4 experts for each input. This sparse MoE approach enables good outputs with efficiency being of paramount importance.
The Gating Network is basically the brains behind the brains—it decides which of the experts in the Mixture of Experts model to respond to a particular input. Upon reception of an input, the gating network assigns scores to all the available experts based on how relevant they are to the task. It then chooses the best performing few, usually the top two to four, and only activates them. The output from these chosen experts is then merged together, each one weighted by its score.
This procedure is important for a number of reasons. First, it optimizes efficiency by utilizing only the experts needed, hence saving computational resources. Second, it optimizes specialization, since every specialist keeps improving in its area of specialization. Third, dynamic routing is used by the system, which adjusts its operation to accommodate the special nature of each input.
There are a number of gating strategies. Soft gating gives fractional weights to all the experts so that they can contribute partially. Hard gating chooses and assesses only the top-ranked few and only looks at the most relevant experts. Learned gating enables the model to learn and enhance its selection approach over time by knowing which experts work best for various tasks.
Ultimately, it is gating that renders the MoE architecture so scalable—allowing trillions of parameter models to be run at an efficiency, without the need for an equivalent explosion in computation.
Think of it like visiting a hospital. You don’t see every doctor—you’re routed to the cardiologist or neurologist depending on your symptoms.
That’s MoE in action: Only the relevant “specialists” (experts) are on duty per task.
Mixture of Experts isn’t just about building bigger models—it’s about building smarter systems that think like a team.
The Mixture of Experts (MoE) architecture has several strong benefits making it a paradigm shift in AI scalability. For one, it is scalable—MoE can be used to create models with trillions of parameters without commensurately scaling computational cost. This is achievable because of its sparse activation mechanism, wherein only a few experts are active at any one time. This goes straight to efficiency since only the most applicable segments of the model are triggered per activity, with resource utilization dramatically decreased.
One additional advantage is specialization. As each specialist is concentrating on one type of input or task, they can develop expert-level proficiency in their areas of specialization, and overall output quality will increase. Parallelism is also a valuable advantage; due to experts being distributed and independently operable, MoE models can execute optimally across various GPUs. Finally, MoE provides flexibility with its dynamic routing mechanism, which adjusts input processing in real-time to suit best performance based on the type of task.
Meta’s Llama 4 models are a testament to the power and practicality of the MoE architecture. These models are designed to deliver high performance, impressive scalability, and cost-efficiency, making them ideal for both enterprise and developer use.
is the most efficient model in the lineup, with 17 billion active parameters and a total of 109 billion parameters across 16 experts. It supports up to a 10 million token context and runs efficiently on a single NVIDIA H100 GPU. This makes it a great choice for summarizing long documents or analyzing large codebases quickly and accurately.
is a step up in capability, with 17 billion active and 400 billion total parameters spanning 128 experts. What makes Maverick stand out is its multimodal capability—it can understand both text and images, making it suitable for tasks that involve visual content. It also supports 12 languages, making it ideal for building multilingual chatbots, generating creative content, and facilitating cross-cultural communication at scale.
Looking ahead, Llama 4 Behemoth is set to be Meta’s most ambitious model yet. Expected to include over 2 trillion total parameters with 288 billion active per inference, Behemoth is designed for cutting-edge AI research and development. Its large-scale multimodal capabilities aim to support next-generation workloads across industries and redefine what AI systems can achieve.
Meta has priced Llama 4 models to be developer-friendly, offering GPT-4-level performance at a fraction of the cost.
| Model | Cost per Million Tokens |
|---|---|
| GPT-4o | $4.38 / million tokens |
| Maverick | ~$0.19–$0.49 / million tokens |
| Llama 4 | Up to 20x more cost-efficient than GPT-4 |
Mixture of Experts (MoE) isn't merely a slick architecture hack—it's an emerging building block of the future of AI. What makes it so strong is its efficiency: it enables the training of gigantic models without requiring equally gargantuan hardware due to its sparse activation trick. It's intelligent, too, since individual experts within the framework get better at doing specific kinds of tasks the more, they're exposed to them, leading to profound specialization
From the scaling standpoint, MoE is scalable by design, allowing for increasing the number of experts without the linear increase in cost of computation. This is cost-efficient with high performance and much lower infrastructure costs compared to classical monolithic models.
Mixture of Experts is like having a dream team of specialists in your AI model—only the right experts respond at the right time. It’s this targeted intelligence that makes MoE models faster, smarter, and vastly more scalable.
Meta’s Llama 4 series show how MoE is transforming theory into practice—with Scout and Maverick already in action, and Behemoth poised to redefine the AI frontier.
The future of efficient AI is already here—and it’s powered by MoE.
