Overcoming the Limitations of Huge AI Models: Llama 4’s Mixture-of-Experts Architecture
Introducing Mixture-of-Experts (MoE) Architecture
Meta constructed the Llama 4 models using a mixture-of-experts (MoE) architecture, which is a way to overcome the limitations of running huge AI models. This approach is inspired by having a large team of specialized workers, where only the relevant specialists activate for a specific job. In the context of Llama 4, this means that instead of a single large model working on every task, multiple smaller models (experts) work together to achieve the same goal.
Reducing Computation Needs
The MoE architecture allows for a reduction in the computation needed to run the model, since smaller portions of neural network weights are active simultaneously. For example, Llama 4 Maverick features a 400 billion parameter size, but only 17 billion of those parameters are active at once across one of 128 experts. Similarly, Scout features 109 billion total parameters, but only 17 billion are active at once across one of 16 experts.
Current Limitations of AI Models
Current AI models have a relatively limited short-term memory. In AI, a context window acts somewhat in this fashion, determining how much information it can process simultaneously. AI language models like Llama typically process this memory as chunks of data called tokens, which can be whole words or fragments of longer words. Large context windows allow AI models to process longer documents, larger code bases, and longer conversations.
Reality Check for Llama’s Reality
Despite Meta’s promotion of Llama 4 Scout’s 10 million token context window, developers have found that using even a fraction of that amount has proven challenging due to memory limitations. Willison reported on his blog that third-party services providing access, like Groq and Fireworks, limited Scout’s context to just 128,000 tokens. Another provider, Together AI, offered 328,000 tokens.
The Need for Resources
Evidence suggests that accessing larger contexts requires immense resources. Willison pointed to Meta’s own example notebook ("build_with_llama_4"), which states that running a 1.4 million token context needs eight high-end Nvidia H100 GPUs.
Testing Troubles
Willison documented his own testing troubles. When he asked Llama 4 Scout via the OpenRouter service to summarize a long online discussion (around 20,000 tokens), the result wasn’t useful. He described the output as "complete junk output," which devolved into repetitive loops.
Conclusion
The MoE architecture used in Llama 4 models is a promising approach to overcoming the limitations of huge AI models. However, it is clear that there are still significant challenges to overcome, particularly when it comes to memory limitations and the need for resources to access larger contexts.
Frequently Asked Questions
Q: What is the Mixture-of-Experts (MoE) architecture?
A: MoE is an approach to building AI models that involves having a large team of specialized workers, where only the relevant specialists activate for a specific job.
Q: How does MoE reduce computation needs?
A: MoE reduces computation needs by having smaller portions of neural network weights active simultaneously, rather than a single large model working on every task.
Q: What is a context window in AI?
A: A context window is a measure of how much information an AI model can process simultaneously.
Q: Why is accessing larger contexts challenging?
A: Accessing larger contexts requires immense resources, including high-end GPUs and significant computational power.
Q: What are some of the limitations of Llama 4 models?
A: Llama 4 models have a relatively limited short-term memory and are challenging to use with large context windows due to memory limitations.
