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 language models can reduce computational costs by 2-4%24times%24 compared to dense models without sacrificing performance%2C making them more efficient in computation-bounded scenarios. However%2C MoE models generally require 2-4%24times%24 times more parameters to achieve comparable performance to a dense model%2C which incurs larger GPU memory requirements and makes MoE models less efficient in I%2FO-bounded scenarios like autoregressive generation. In this work%2C we propose a hybrid dense training and sparse inference framework for MoE models (DS-MoE) which achieves strong computation and parameter efficiency by employing dense computation across all experts during training and sparse computation during inference. Our experiments on training LLMs demonstrate that our DS-MoE models are more parameter-efficient than standard sparse MoEs and are on par with dense models in terms of total parameter size and performance while being computationally cheaper (activating 30-40%25 of the model's parameters). Performance tests using vLLM show that our DS-MoE-6B model runs up to %241.86times%24 faster than similar dense models like Mistral-7B%2C and between %241.50times%24 and %241.71times%24 faster than comparable MoEs%2C such as DeepSeekMoE-16B and Qwen1.5-MoE-A2.7B.?quality=80&w=800)
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