Mistral AI (Mixtral-8x7B): Performance, Benchmarks

In the field of large language models (LLMs), there has been a shift from dense architectures, where all neurons participate in processing each piece of information, to mixture-of-experts (MoE) architectures, which allow for more efficient use of resources. MoE architecture involves a gating network that decides which "experts" to route tokens to based on their content. This enables the model to focus its computational power on relevant areas while reducing overall compute time and cost. Mistral 8X7B is an example of an LLM utilizing MoE architecture, with a total of 46.7 billion parameters distributed across eight "experts." The non-feedforward blocks are executed for each token, resulting in only two out of the eight experts being utilized per token. This allows for more efficient use of resources and faster inference times compared to dense models like Llama 2 70B. However, there are limitations to this approach, particularly when it comes to knowledge compression within the model. Due to having fewer parameters than some other LLMs, Mistral may not perform as well on tasks that require extensive knowledge storage and retrieval. Further research is needed to optimize MoE architectures for various applications and improve their overall performance.