Mixture of Experts (MoE) models have emerged as a primary solution for reducing the computational cost of Large Language Models (LLMs). "Scaling Laws for Fine-Grained Mixture of Experts", Jakub Krajewski, Jan Ludziejewski, and their colleagues from the University of Warsaw and IDEAS NCBR analyze the scaling properties of MoE models, incorporating an expanded range of variables. …
Large Language Models (LLMs) like GPT-4, ChatGPT, and J1-Jumbo have revolutionized natural language processing, enabling unprecedented performance on a wide range of tasks. However, the high cost of querying these LLM APIs is a major barrier to their widespread adoption, especially for high-throughput applications. …
Large language models (LLMs) have demonstrated impressive capabilities across a wide range of applications. However, no single model can optimally address all tasks, especially when considering the trade-off between performance and cost. This has led to the development of LLM routing systems that leverage the strengths of various models. …