Mixture-of-experts (MoE) is revolutionizing how large AI models operate by allowing them to run more efficiently and intelligently. It’s a system where specialized sub-models—called experts—are activated only when needed, saving computing power and accelerating performance. Now adopted by leaders like Meta, DeepSeek, and likely OpenAI, MoE might become the new norm for high-performance AI. Here’s everything you need to know about how it works, why it matters, and where it’s headed. At 3minread.com, we help you stay ahead of trends in crypto, AI, and beyond.

What Is a Mixture-of-Experts Model?

A smarter way for AI models to operate by using specialized sub-networks to handle specific tasks

A mixture-of-experts (MoE) model is a machine learning architecture composed of multiple specialized sub-models—referred to as "experts"—and a routing mechanism that selects which experts to use for a given input. Unlike traditional dense models that activate every parameter for every input, MoE models selectively activate only a few experts, significantly reducing computational load.

Think of it like replacing one generalist AI brain with a team of specialists and a smart manager who picks the right ones for each job. If you're asking an AI, "What is a llama?", the model might activate an expert on animal names, one on sentence structure, and one on punctuation—skipping the experts on calculus or coding. This not only saves energy but boosts performance, especially in large language models (LLMs).

The architecture has gained traction among leading models like DeepSeek V3 and Meta’s Llama 4 Maverick, with strong speculation that OpenAI’s GPT-4 also relies on it. MoE offers a powerful new way to scale AI without breaking the bank on compute resources.

How Does a Mixture-of-Experts Model Work?

Selective activation of neural sub-networks keeps AI both powerful and efficient

To appreciate how MoE works, it's useful to contrast it with traditional dense models. Dense models activate every parameter in the network for every token in an input prompt. For instance, a prompt like "What is a llama?" activates all 70 billion parameters in a dense model like Llama 3 70B five times—once for each token.

This leads to a lot of wasted computation, especially since most parameters may not be relevant to the question at hand. That’s where MoE models shine. They use a routing function—a kind of internal switchboard—that decides which experts to activate for each token. This concept is called sparsity, and MoE models are often referred to as sparse models because they only activate a subset of their parameters at any time.

Take Llama 4 Maverick as an example. It has 128 experts per layer, yet only a few are activated per token. This means that even though the total model size is massive (over 400 billion parameters), it only uses 17 billion per token—matching or even outperforming smaller dense models at a lower computational cost.

Experts in MoE aren’t based on subject areas like history or physics. Instead, they specialize in linguistic functions: handling verbs, punctuation, conjunctions, proper nouns, etc. This division of labor allows the model to handle complex prompts more intelligently and efficiently than a one-size-fits-all dense model.

Benefits and Challenges of MoE Models

Cheaper to run, harder to train—MoE is a high-reward, high-effort architecture

MoE models shine during inference—the phase where an AI generates output based on a prompt. Because they activate fewer parameters, they consume less compute and energy, which translates into faster response times and reduced operational costs. For AI companies, this efficiency is crucial for scaling services to millions of users.

However, training MoE models is significantly more complex. Each expert must be trained adequately, and the routing function must learn when to send tokens to which expert. It’s a balancing act that requires more compute, data, and fine-tuning than dense models.

Moreover, MoE models are typically massive in size. Even though they only activate a small portion at a time, the entire model must reside in memory. That means you’ll need serious hardware—think enterprise-grade GPUs with hundreds of gigabytes of VRAM—to run them effectively. This makes them unsuitable for local or mobile use cases where resources are limited.

Despite these challenges, the payoff is worth it. Once trained, MoE models are not only more powerful but also more economical to operate at scale, making them a strategic choice for companies running large AI workloads.

Why MoE Matters for the Future of AI

As AI continues to grow, MoE offers a sustainable path forward

The importance of MoE models goes beyond just cost-efficiency—they’re paving the way for the next generation of intelligent systems. The largest and most powerful open models today, like Llama 4 Maverick and DeepSeek R1, rely on MoE architectures. And even though companies like OpenAI and Google haven’t officially confirmed their use of MoE, industry experts believe it's only a matter of time—if they’re not already using it.

MoE allows for more specialized and fine-grained processing, enabling better results with fewer computational resources. This means AI systems can grow in capability without growing exponentially in cost, energy consumption, or hardware requirements. It's a big step toward making super-powerful AI accessible and sustainable.

For businesses, this means the possibility of more cost-effective AI integrations. For researchers, it offers a new way to design models that are both scalable and flexible. And for users, it promises faster, more accurate interactions with AI tools.

What's Next for Mixture-of-Experts Models?

MoE is still maturing, but its role in the future of AI looks inevitable

MoE isn’t exactly new—it’s been around in academic and experimental settings for years. But only recently has the tech and expertise caught up enough to make large-scale deployment feasible. That’s why we’re now seeing models like Llama 4 Scout and Maverick implement MoE at industrial scale.

As hardware capabilities improve and training strategies become more refined, expect to see even more models adopting this architecture. Open-source communities and commercial players alike are investing heavily in MoE research, aiming to strike the right balance between training difficulty and inference performance.

In the long term, MoE might become the standard architecture for large-scale models, with dense models reserved for smaller, locally-run applications. This evolution could democratize access to high-performance AI, shifting the paradigm from brute-force scaling to intelligent, efficient design.