{"id":1002408,"date":"2024-01-28T22:43:45","date_gmt":"2024-01-29T06:43:45","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&p=1002408"},"modified":"2024-01-28T22:43:45","modified_gmt":"2024-01-29T06:43:45","slug":"who-says-elephants-cant-run-bringing-large-scale-moe-models-into-cloud-scale-production-2","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/who-says-elephants-cant-run-bringing-large-scale-moe-models-into-cloud-scale-production-2\/","title":{"rendered":"Who Says Elephants Can’t Run: Bringing Large Scale MoE Models into Cloud Scale Production"},"content":{"rendered":"
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Mixture of Experts (MoE) models with conditional execution of sparsely activated layers have enabled training models with a much larger number of parameters. As a result, these models have achieved significantly better quality on various natural language processing tasks including machine translation. However, it remains challenging to deploy such models in real-life scenarios due to the large memory requirements and inefficient inference. In this work, we introduce a highly efficient inference framework with several optimization approaches to accelerate the computation of sparse models and cut down the memory consumption significantly. While we achieve up to 26x speed-up in terms of throughput, we also reduce the model size almost to one eighth of the original 32-bit float model by quantizing expert weights into 4-bit integers. As a result, we are able to deploy 136x larger models with 27% less cost and significantly better quality compared to the existing solutions. This enables a paradigm shift in deploying large scale multilingual MoE transformers models replacing the traditional practice of distilling teacher models into dozens of smaller models per language or task.<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Mixture of Experts (MoE) models with conditional execution of sparsely activated layers have enabled training models with a much larger number of parameters. As a result, these models have achieved significantly better quality on various natural language processing tasks including machine translation. However, it remains challenging to deploy such models in real-life scenarios due to 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