As Large Language Models (LLMs) like ChatGPT, Claude, and Gemini grow increasingly powerful, researchers are exploring new ways to scale models without increasing compute costs. One of the most promising innovations is the Mixture of Experts (MoE) architecture, a modular approach that makes LLMs both faster and more efficient.

In this post, we’ll explain how MoE differs from the traditional Transformer architecture, how expert selection works, and the challenges that come with this powerful design.

🔍 What is a Transformer?

Transformers are the foundation of most modern LLMs. The Transformer architecture uses:

• Self-attention mechanisms, which help the model understand the relationships between words in a sentence, regardless of their position.
• Feed-forward neural networks (FFNs), which process and transform the information within each layer.

Transformers are powerful because they can capture complex patterns in language, but they also activate every layer and parameter for every input token. This full activation makes them computationally expensive, especially as model sizes grow.

🔍What is a Mixture of Experts (MoE)?

A Mixture of Experts (MoE) is an advanced architecture built on top of Transformers. Its main idea is to make the model more efficient by activating only a small subset of specialised networks called experts for each input.

Each expert is a lightweight feed-forward neural network, and only a few are selected at a time based on the input. This selection is handled by a Router, which chooses the most relevant experts dynamically for each token during inference.

By using only a few experts per input, MoE models can:

• Dramatically reduce computational cost,
• Increase overall model capacity,
• And allow different experts to specialise in different types of data or tasks.

This modular approach enables models to scale up to hundreds of billions of parameters while keeping inference fast and efficient.

🧠 How Are Experts Selected in MoE?

Expert selection is done using a Router, which acts like a multi-class classifier. Here’s how it works:

1. The Router receives token embeddings as input.
2. It computes a softmax distribution over the available experts.
3. It selects the top K experts (usually 1 or 2) based on those scores.
4. Only the selected experts process the token.

The Router is trained jointly with the rest of the model, learning to choose the most suitable experts based on the input.

Real-World Example: 

Mixtral 8x7B by Mistral AI

A popular real-world implementation of MoE is Mixtral 8x7B by Mistral AI:

• It features 8 expert networks per MoE layer.
• For each token, only 2 experts are activated.
• This allows the model to scale up to tens of billions of parameters while keeping inference efficient.

Mixtral has demonstrated state-of-the-art performance across multiple benchmarks — a testament to the power of MoE.

Qwen3-235B-A22B by Alibaba

• A cutting-edge open-source MoE LLM with a staggering 235 billion parameters.
• Uses only 22 billion active parameters per token, thanks to MoE’s sparse activation strategy.
• Recent evaluations show Qwen3-235B-A22B:
  • Outperforms other top-tier open models like DeepSeek-V3 and DeepSeek-R1.
  • Rivals leading closed-source models such as GPT-4o and Gemini 2.5 Pro in reasoning, coding, and instruction-following tasks.
• Its combination of efficiency, performance, and openness makes it highly attractive for enterprise adoption and deployment.

Both Mixtral and Qwen3-235B showcase how MoE architectures are not just theoretical advancements — they are actively reshaping the landscape of AI model design and deployment.

Why MoE Matters for the Future of LLMs

MoE architectures represent a significant step forward in model design. Here’s why they matter:

✅ Faster inference due to sparse expert activation

✅ Higher capacity with lower compute requirements

✅ Improved specialisation and task adaptability

✅ Scalable to massive parameter sizes

Although MoE introduces complexity during training, careful design of routers, expert balancing, and capacity control make it a powerful tool for next-generation AI systems.