Mixture of Experts (MoE) is the neural network architecture behind the most powerful AI models of the moment: Mixtral, Kimi K2.6, and, according to researchers, versions of GPT-4. Understanding how it works is no longer just for academics: it's essential knowledge for any developer or AI Engineer who wants to understand why some models are so capable and efficient at the same time.

In this article we explain what MoE is, how it differs from traditional dense transformers, and why its massive adoption in the models launched in 2026 makes it a key skill for developers in LATAM.

What is Mixture of Experts (MoE)

Mixture of Experts is a type of neural network architecture in which the model does not activate all of its parameters for each token it processes. Instead, it uses a component called a router that decides which "experts" (specialized submodels) should process each fragment of the input.

The central idea is simple: instead of a single enormous model that does everything, you have many specialized experts and an intelligent mechanism that decides who responds to what. Only a few experts are activated per token, which makes the computation much more efficient.

How MoE works: routers and experts

A MoE layer has two key components:

• Experts: They are smaller neural networks (generally FFNs, feed-forward networks) that specialize in different types of information. A model can have hundreds or thousands of them.

• Router (or gating network): It's a lightweight component that analyzes each input token and decides which experts to send it to. Normally only 2 to 8 experts are activated per token, regardless of how many the model has in total.

This design allows the model to have an enormous total parameter capacity (for example, 1 trillion in Kimi K2.6) but only use a fraction of them in each inference, drastically reducing the computational cost.

MoE vs. dense Transformers: the key differences

Traditional dense transformers (like the first GPTs or BERT) activate all their parameters for each token. That makes them predictable but costly at scale. MoE introduces a way to scale without that proportional cost:

According to researchers from Mixtral of Experts (Mistral AI, 2024), a MoE model of 46.7B total parameters can match or surpass a dense transformer of 70B by activating only 12.9B active parameters per token.

Models that use MoE in practice

The MoE architecture stopped being experimental: it's the dominant standard in the top models of 2025-2026.

• Mixtral 8x7B and 8x22B (Mistral AI): The first popular open source models with MoE. With 8 experts and activation of 2 per token, they achieved state-of-the-art performance with less computation.

• Kimi K2.6 (Moonshot AI): MoE architecture with 1 trillion total parameters, 256K tokens of context, capable of orchestrating up to 300 agents in parallel. Launched in April 2026 under a Modified MIT license.

• DeepSeek V4: Chinese open source model launched at the end of 2025, with a MoE architecture optimized for reasoning, which challenged the supremacy of Western proprietary models in math and code benchmarks.

• GPT-4 (OpenAI): Although OpenAI never officially confirmed the architecture, multiple industry sources indicate that GPT-4 uses MoE internally (SemiAnalysis).

Why MoE is relevant for developers in LATAM

If you work with language models, whether integrating APIs, fine-tuning, or building applications, understanding MoE helps you make better decisions:

• Model selection: You know why Mixtral or Kimi K2.6 can be more efficient than dense models of similar size for certain tasks.

• Inference optimization: In setups with limited resources (typical in LATAM startups), MoE models offer better performance per dollar spent on GPU.

• Agent design: Kimi K2.6 can orchestrate 300 agents in parallel precisely because its MoE architecture allows experts to be reused efficiently. Understanding this impacts how you design multi-agent workflows.

• Job descriptions: In 2026, "MoE", "mixture of experts", and "sparse models" appear with increasing frequency in AI Engineer and ML Engineer postings at Argentine companies and those in the region.

If you're interested in going deeper into how these architectures are deployed in production, you might be interested in this article about LLMOps and how to manage language models in production in LATAM.

Challenges of MoE: what nobody tells you

The architecture is not perfect. The main technical challenges are:

• Load balancing: If the router always chooses the same experts, some are underutilized and the model doesn't take advantage of its capacity. It's solved with auxiliary loss techniques.

• RAM memory: Although the inference is efficient in FLOPs, all the parameters must be loaded in memory. A MoE model of 1B parameters requires much more RAM than a dense one of the same active size.

• Training complexity: Training a MoE model from scratch is more complex than a dense transformer, with more hyperparameters to tune.

For practical use in Argentina and LATAM, these challenges are mitigated by using already-trained models via APIs (Mistral, Moonshot) or through quantization to run open source models on local hardware.

AI training at Coderhouse

If you want to understand in depth how these architectures work and apply them in real projects, Coderhouse has courses for different levels:

• Introduction to Artificial Intelligence Course: ideal for understanding the fundamentals of language models, including transformers and the architectural differences between models.

• AI Engineering Course: for developers who want to work with LLMs in production, including integrating APIs of MoE models like Mixtral or Kimi K2.6.

• AI Automation Career: complete training to work with AI agents and automation flows using the most advanced models on the market.

Frequently asked questions

What does "Mixture of Experts" mean in Spanish?

It translates literally as "Mezcla de Expertos". In practice, it refers to a neural network architecture where different specialized submodels (the "experts") are selectively activated according to the type of input, coordinated by a component called a router or gating network.

Is MoE only for language models (LLMs)?

No. The MoE architecture has existed since the 1990s (Jacobs et al., 1991) and has been applied in computer vision, recommendation systems, and multimodal models. However, its massive adoption in recent years occurred mainly in large-scale LLMs, where the efficiency benefits are most evident.

Can I run a MoE model locally in Argentina?

Yes, with the limitations of available hardware. Mixtral 8x7B in a quantized version (GGUF/Q4) can be run on a PC with 24GB of RAM or on an RTX 3090/4090 GPU. For larger models like Kimi K2.6 or DeepSeek V4, you need server-level hardware or to access it via API. Platforms like Ollama and LM Studio make it easier to run quantized MoE models locally.

Do all large models use MoE?

Not all, but it's the dominant trend. Models like LLaMA 3 (Meta) in its 70B version are dense transformers. The choice depends on the design goals: MoE prioritizes computational efficiency and scale, while dense models are more predictable and easier to optimize for inference on specific hardware.

What is the "router" in a MoE model and how does it decide which expert to use?

The router is a small neural network (generally a linear layer with softmax) that takes the representation of the current token as input and produces a probability distribution over the available experts. The K experts with the highest probability are selected (top-K routing). During training, an auxiliary loss is used to prevent the router from always choosing the same experts and to guarantee that all of them learn.