Diversity is the spice of … AI?

Pros of a single LLM:

- Simpler and more straightforward architecture

- Easier to train and deploy a single model

Pros of MoE:

1. Improved performance:

- The combination of specialized expert sub-models can outperform a single large, generalist model like GPT on many tasks. [1][4]

- MoE models like GPT-4 (rumored to be 1.76 trillion parameters across 8 experts) can achieve higher performance than a single 1.76T model. [3][4]

2. Reduced training costs:

- MoE models can be trained with less compute power compared to a single large model like GPT-3. [4]

3. Scalability:

- Adding more expert sub-models allows MoE to scale to even larger model sizes, like the rumored 1.76 trillion parameter GPT-4. [4]

Cons of MoE:

- More complex architecture and training process

- Potential for load imbalance and training instability if not designed properly[1]

In summary, the MoE approach offers significant advantages in terms of performance, efficiency, and scalability compared to a single LLM, but requires more sophisticated model design and training. The choice between the two depends on the specific requirements and constraints of the application. [1][4]

Citations:

[1] https://www.superannotate.com/blog/mixture-of-experts-vs-mixture-of-tokens

[2] https://deci.ai/blog/model-merging-moe-frankenmerging-slerp-and-task-vector-algorithms/

[3] https://alexandrabarr.beehiiv.com/p/mixture-of-experts

[4] https://deepgram.com/learn/mixture-of-experts-ml-model-guide

[5] https://www.reddit.com/r/LocalLLaMA/comments/16htb5m/why_are_all_the_other_llms_so_inferior_to_gpt4/