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+language+models+can+reduce+computational+costs+by+2-4%2524times%2524+compared+to+dense+models+without+sacrificing+performance%252C+making+them+more+efficient+in+computation-bounded+scenarios.+However%252C+MoE+models+generally+require+2-4%2524times%2524+times+more+parameters+to+achieve+comparable+performance+to+a+dense+model%252C+which+incurs+larger+GPU+memory+requirements+and+makes+MoE+models+less+efficient+in+I%252FO-bounded+scenarios+like+autoregressive+generation.+In+this+work%252C+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%2525+of+the+model%27s+parameters).+Performance+tests+using+vLLM+show+that+our+DS-MoE-6B+model+runs+up+to+%25241.86times%2524+faster+than+similar+dense+models+like+Mistral-7B%252C+and+between+%25241.50times%2524+and+%25241.71times%2524+faster+than+comparable+MoEs%252C+such+as+DeepSeekMoE-16B+and+Qwen1.5-MoE-A2.7B.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.f57859df611d92a6cfeb49626e015196%3frik%3d3t0c8HGn6xiHDw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Sparse+Inference&simid=608020735648234199&FORM=IRPRST&ck=3BFA51AF43C09665843D669138F5568C&selectedIndex=13&itb=0)
