Oct. 31, 2023, 1:34 a.m. | Synced

Synced syncedreview.com

A research team from Institute of Science and Technology Austria (ISTA) and Neural Magic Inc. introduces the QMoE framework. This innovative framework offers an effective solution for accurately compressing massive MoEs and conducting swift compressed inference, reducing model sizes by 10–20×, achieving less than 1 bit per parameter.


The post MoE: Revolutionizing Memory-Efficient Execution of Massive-Scale MoE Models first appeared on Synced.

ai artificial intelligence austria deep-neural-networks framework inference institute large language model machine learning machine learning & data science magic massive memory mixture of experts ml moe neural magic per research research team scale science science and technology solution swift team technology

More from syncedreview.com / Synced

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Senior Machine Learning Engineer

@ Samsara | Canada - Remote