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Researchers from DbrxMosaicAI and Meta explored the Mixture-of-Experts (MoE) architecture for efficient training and inference using tools like MegaBlocks in the PyTorch framework. The MoE approach aims to reduce inference cost, memory usage, and enable lifelong learning for models. Efforts have been made to scale transformer models efficiently, considering computational costs in feedforward layers.
Efficient Deployment of Large-Scale Transformer Models: Strategies for Scalable and Low-Latency Inference https://t.co/C2c7X7OaAT #inference #models #latency #attention #memory #study #model #multiquery #layouts #transformer https://t.co/o11Wd37sUe
Mixture of A Million Experts: Revolutionizing Transformer Architectures Scaling of transformer models has been a key driver of progress. However, this scaling has come with significant computational costs, particularly in the feedforward (FFW) layers that account for a large… https://t.co/VEs7E9Xpc7
The Mixture of a Million Experts paper is a straight banger. Reduces inference cost and memory usage, scales to millions of experts, oh and just happens to overcome catastrophic forgetting and enable life long learning for the model. Previous MOE models never got past 10k… https://t.co/PORrrdl5HT
