what does a "dense model" mean regarding LLMs

Based on the search results, a "dense model" in the context of large language models (LLMs) refers to a model architecture that uses a standard, fully-connected neural network layer as the feed-forward network component, as opposed to a Mixture-of-Experts (MoE) architecture. The key points are:
  • Dense models have a standard, dense feed-forward network, whereas MoE models use a sparse, Mixture-of-Experts feed-forward network.
  • Dense models generally have more FLOPs (floating-point operations) and are more computationally expensive compared to MoE models with a similar number of trainable parameters.
  • However, the search results suggest that a large, dense model and a smaller, MoE model with comparable parameters can exhibit similar performance and overfitting trends. This indicates the MoE architecture can be used to effectively approximate the behavior of larger, more computationally expensive dense models.
  • The advantage of the MoE approach is that it can achieve similar performance to dense models but with significantly reduced computational cost and improved throughput. This makes MoE models a more efficient and affordable alternative for training and fine-tuning large language models.
In summary, a "dense model" refers to a standard, fully-connected neural network architecture for LLMs, whereas a "MoE model" uses a more efficient Mixture-of-Experts approach, which can match the performance of dense models while being more computationally lightweight.
what is the difference between a dense model and a sparse model in llms
how does the performance of a dense model compare to a sparse model in llms
what are the advantages and disadvantages of using a dense model in llms