What is a mixture of experts model and why are top AI companies using it?
Mixture of Experts (MoE) is a machine learning setup that breaks a massive neural network down into smaller, specialized sub-networks known as “experts.” Instead of forcing one giant model to handle every type of request, MoE uses a built-in gating mechanism to route incoming data to the specific expert best suited for the task. This means the system only activates a fraction of its total network at any given time. As a result, AI models run faster, process information more efficiently, and consume significantly less memory.
Many of the industry’s top AI models rely on this architecture. DeepSeek built its highly efficient model using MoE, and OpenAI relies on it for GPT-4 and its successors, including the massive GPT-5 which reportedly packs over 500 billion parameters. Meta adopted the same strategy for its Llama 4 lineup released in April 2025. For context, Meta’s Scout model balances 16 experts across 109 billion parameters, while its Maverick model scales up to 128 experts and 402 billion parameters.
Optimizing AI Training
MoE fits into a larger trend focused on improving how we train large language models (LLMs). While models can theoretically scale to trillions of parameters, raw size isn’t everything. Modern AI development prioritizes efficiency, transparency, and scalability, which becomes especially vital when fine-tuning a model for a specific task.
Rather than retraining billions of parameters from scratch, developers turn to techniques like low-rank adaptation. This method targets a tiny fraction of the parameters to achieve meaningful updates. For instance, fine-tuning a 175-billion-parameter model using low-rank adaptation might only require updating 17.5 million parameters, saving enormous amounts of computing power.
Meanwhile, other developers are shifting their focus to small language models. Usually containing under a billion parameters, these compact models need far fewer resources and can often be trained in just a few weeks. Alongside these architectural shifts, reinforcement learning continues to gain traction, allowing models to essentially figure out the most effective reasoning pathways on their own.