Mixture of Experts: Scaling LLMs with a Team of Specialists
A Mixture of Experts (MoE) model isn't one giant brain but a team of specialists, routing each task to the most qualified sub-network. This allows large language models to have a massive number of parameters for knowledge, but only activate a small, computationally cheap fraction for any given input. The footgun is mistaking the total parameter count for the active parameters used during inference; MoE models are sparsely activated.
A Mixture of Experts (MoE) model operates like a committee of specialists rather than a single monolithic brain. It uses a "gating network" to route each piece of data—like a token in a sentence—to the most relevant "expert" sub-networks. This architecture is key to scaling modern LLMs, allowing them to possess a huge total parameter count for vast knowledge while only using a fraction for any single token. This dramatically reduces computational cost compared to a dense model of equivalent size. Don't mistake an MoE's total parameter count for its inference cost; a model with 50B total parameters might only use 14B active ones, performing like a much smaller model.
Read the original → Wikipedia: Mixture of experts
- #mixture of experts
- #llm
- #model architecture
- #sparse activation
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