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Model Multitask Illustratio
%252C+with+Transformer+architectures+have+ushered+in+a+new+era+of+Generative+AI+across+various+industries.+Industry+and+research+community+have+witnessed+a+large+number+of+new+applications%252C+based+on+those+foundation+models.+Such+applications+include+question+and+answer%252C+customer+services%252C+image+and+video+generation%252C+and+code+completions%252C+among+others.+However%252C+as+the+number+of+model+parameters+reaches+to+hundreds+of+billions%252C+their+deployment+incurs+prohibitive+inference+costs+and+high+latency+in+real-world+scenarios.+As+a+result%252C+the+demand+for+cost-effective+and+fast+inference+using+AI+accelerators+is+ever+more+higher.+To+this+end%252C+our+tutorial+offers+a+comprehensive+discussion+on+complementary+inference+optimization+techniques+using+AI+accelerators.+Beginning+with+an+overview+of+basic+Transformer+architectures+and+deep+learning+system+frameworks%252C+we+deep+dive+into+system+optimization+techniques+for+fast+and+memory-efficient+attention+computations+and+discuss+how+they+can+be+implemented+efficiently+on+AI+accelerators.+Next%252C+we+describe+architectural+elements+that+are+key+for+fast+transformer+inference.+Finally%252C+we+examine+various+model+compression+and+fast+decoding+strategies+in+the+same+context.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.762a256c921a65bc0dc1f82de8972b71%3frik%3dWeV6LiSpa2LpoA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Ai+Foundation+Model+Multitask+Illustratio&FORM=IRPRST&ck=9D86CD50D36590EFCB6CEC090A26F016&selectedIndex=14&itb=0)
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