The Mixture of Experts (MoE) is a neural network architecture design that integrates layers of experts/models within the Transformer block. It dynamically routes input tokens to a subset of experts for computation. MoE offers faster inference and high VRAM usage. Stanford University, Microsoft Research, and Google Research have published a paper on MegaBlocks, which is about efficient sparse training with MoE.
[LG] MegaBlocks: Efficient Sparse Training with Mixture-of-Experts T Gale, D Narayanan, C Young, M Zaharia [Stanford University & Microsoft Research & Google Research] (2022) https://t.co/6Lvg8QUfen - Mixture-of-Experts (MoE) models route input tokens dynamically to expert… https://t.co/bCZTs6tu6o
Lots of confusion about MoEs out there. IIUC: - Faster inference as a fixed number of experts is activated per token (if sparse). E.g., if n=1, just the most appropriate expert is activated. - High VRAM usage; all experts need to be loaded. - Work well when you run them on many…
What is Mixture-of-Experts (MoE)? MoE is a neural network architecture design that integrates layers of experts/models within the Transformer block. As data flows through the MoE layers, each input token is dynamically routed to a subset of the experts for computation. This… https://t.co/56mKkrHL34 https://t.co/AnYeITgHVi
What is Mixture-of-Experts (MoE)? MoE is a neural network architecture design that integrates layers of experts/models within the Transformer block. As data flows through the MoE layers, each input token is dynamically routed to a subset of the experts for computation. This… https://t.co/uDvjTeuaAC https://t.co/CKNPLQahBx
This is the paper you need to read to understand Mixture-of-Experts (MoE) training https://t.co/mXhVIDJwDw https://t.co/y6ZtP5fUi2
the original "Mixture of Experts" https://t.co/d8pFx6wBbT
Hierarchical Mixture of Experts https://t.co/CbonzMvD5o
Seems like a nice day to read some MoE papers https://t.co/Ixb0doT5Cw https://t.co/097JULZHyl
The Mixtral of experts is here!