Multimodal Models vs. Mixture-of-Experts Models: What Is the Difference?

Introduction

As AI models become more advanced, terms such as multimodal models and Mixture-of-Experts (MoE) models are increasingly common. Although both can support sophisticated AI applications, they describe different aspects of an AI system.

A multimodal model refers primarily to the types of information a model can process, while a Mixture-of-Experts model describes an architectural approach for efficiently processing information. Understanding this distinction is important when evaluating AI models and infrastructure.

What Are Multimodal Models?

A multimodal model can process or work with multiple types of data, known as modalities. These may include:

1. Text: 

Documents, questions, instructions, and conversations.

2. Images: 

Photos, diagrams, charts, and screenshots.

3. Audio: 

Speech, sounds, and recordings.

4. Video: 

Moving visual content, sometimes combined with audio.

5. Other data: 

Depending on the system, this may include structured data or sensor information.

For example, a multimodal AI system could analyse an image, understand a written question about it, and generate a text response. The key characteristic is the model’s ability to understand and connect information from different modalities.

What is a Mixture-of-Experts Model?

A Mixture-of-Experts model uses multiple specialized components called experts. Instead of activating the entire model for every input, a routing mechanism determines which experts should process a particular piece of information.

This creates a form of conditional computation. For example, an MoE model might contain many expert networks, but only a subset may be activated for a particular token or input. This allows the overall model to contain a large number of parameters without requiring all of them to be used for every calculation. MoE architecture can therefore help increase model capacity while managing computational requirements.

The Key Difference

The simplest way to understand the difference is:

Multimodal describes what information a model can process, while MoE describes how the model’s computational architecture operates. A multimodal model might accept text and images, for example. An MoE model might use specialized experts to process different parts of the input. Importantly, these concepts are not mutually exclusive.

Can a Model Be Both?

Yes. A model can be both multimodal and Mixture-of-Experts. For example, an AI system could be designed to process text, images, and audio while also using an MoE architecture internally.

In this situation, multimodality describes the model’s input and output capabilities, while MoE describes part of its underlying architecture. This distinction matters because businesses should not treat the two terms as competing categories.

Differences in Infrastructure Requirements

The infrastructure requirements can also differ.

Multimodal models may require substantial computing resources because processing images, video, or audio can involve significant amounts of data. Large context windows and multiple input types can increase memory and processing requirements.

MoE models, meanwhile, can have very large parameter counts while activating only a portion of their parameters for individual inputs. However, efficient routing and communication between components can become important, particularly when the model is distributed across multiple GPUs. High-performance GPUs, fast memory, and efficient networking can therefore be important for both approaches.

Conclusion

Understanding the difference helps businesses evaluate AI solutions more accurately. It is also possible for a single model to combine both approaches, creating a system capable of processing multiple modalities while using conditional computation to manage its computational workload.