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1、1The 2021 State of AI Infrastructure SurveyAll rights reserved to Run:ai.No part of this content may be used without express permission of Run:ai.www.run.ai2Large Teams and Big BudgetsBig Plans for AI and Limited Confidence8Demographics11Introduction and Key Findings3GPU Farm Size and Server Locatio

2、nsSize of Research Teams and Access to On-Demand GPU Compute as NeededGPU and AI Hardware Utilization and Resource Allocation IssuesCompanies of All Sizes Struggle with Hardware UtilizationTools Used to Optimize GPU Allocation Between UsersContainers and Kubernetes for AI WorkloadsCountry of Residen

3、ceCompany Size,Job Functions,Seniority and IndustryActionable Steps Based on the Key FindingsModels Making it to ProductionMain Challenges for AI DevelopmentPlans to Increase GPU Capacity or Additional AI InfrastructureConfidence in AI infrastructure Stack Set-up to Build,Train and MoveThis Guide Co

4、vers:All rights reserved to Run:ai.No part of this content may be used without express permission of Run:ai.www.run.aiMost research around the state of the Artificial Intelligence(AI)industry talks about the same few facts:AI is still very immature,models rarely make it to production,and challenges

5、remain for data scientists and research teams around creating the right infrastructure and setting up AI for success.To discover whether these pervasive ideas are still gospel in 2021,we commissioned a survey of 211 data scientists,AI/Machine Learning/IT practitioners and system architects from 10 c

6、ountries around the world.We spoke primarily with experts from large enterprise companies with over 5,000 employees,and some with as many as 10,000.We asked these enterprises to open up about the technologies they use,the challenges they face with AI and the size of not only their AI budget,but also