OpenAI has not publicly disclosed whether ChatGPT-4 (also referred to as GPT-4) is based on a Mixture of Experts (MoE) architecture. However, there are some indications that GPT-4 may use a hybrid approach that could incorporate elements of MoE while also relying on a denser model structure than its predecessor.
1. What Is MoE?
MoE (Mixture of Experts) is an architecture where different “expert� subnetworks handle different parts of an input, allowing for greater efficiency in large language models. Instead of activating all parameters for every input, an MoE model activates only a subset of them, which helps with scalability while maintaining high performance.
2. GPT-3 and MoE
• GPT-3 was a fully dense model, meaning all of its parameters were used for every computation.
• OpenAI did experiment with MoE models around the same time as GPT-3 (e.g., in research papers), but GPT-3 itself was not MoE-based.
3. Is GPT-4 Using MoE?
• GPT-4 is rumored to be a hybrid model. Some reports suggest that OpenAI may have implemented a partially sparse MoE architecture, where only a subset of experts is used per query, but this has not been confirmed.
• GPT-4-turbo and cost efficiency: OpenAI has suggested that GPT-4-turbo is cheaper to run than GPT-4, which hints at some form of MoE or other efficiency optimizations. MoE is often used to reduce computational costs while maintaining performance.
• Scaling laws favor MoE: Given that AI researchers are pushing towards larger-scale models, an MoE-based GPT-4 (or a mix of MoE and dense layers) would align with trends in AI model development.
4. Official OpenAI Statement?
• OpenAI has not confirmed whether GPT-4 uses MoE.
• However, Microsoft’s research paper on GPT-4 mentioned that it is “a mixture of dense and MoE models�, implying that GPT-4 might use some form of MoE but is not purely an MoE model.
Conclusion:
GPT-4 may incorporate some elements of MoE, but it is not confirmed to be a fully MoE-based model. It is likely a hybrid model, combining MoE techniques with traditional dense transformer layers for efficiency and performance.
