{"id":2988030,"date":"2026-11-10T08:38:05","date_gmt":"2026-11-10T16:38:05","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=2988030"},"modified":"2026-11-10T08:56:40","modified_gmt":"2026-11-10T16:56:40","slug":"impact-of-desktop-resources-on-editors-productivity-and-user-experience","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis\/administration\/impact-of-desktop-resources-on-editors-productivity-and-user-experience","title":{"rendered":"Impact of desktop resources on editors\u2019 productivity and user experience"},"author":226402,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[37501],"tags":[],"industry":[],"product":[421922,36561],"class_list":["post-2988030","blog","type-blog","status-publish","format-standard","hentry","category-administration","product-arcgis","product-arcgis-pro"],"acf":{"authors":[{"ID":226402,"user_firstname":"Derrick","user_lastname":"Burke","nickname":"Derrick Burke","user_nicename":"dburke","display_name":"Derrick Burke","user_email":"dburke@esri.com","user_url":"","user_registered":"2021-05-03 22:15:13","user_description":"Derrick Burke is an Architect at Esri who helps organizations design, build, and sustain ArcGIS systems that deliver business value. He is passionate about sharing knowledge and helping customers maximize the value of their GIS investments. In his free time, he enjoys surfing, sailing, and mountain biking.","user_avatar":"<img data-del=\"avatar\" src='https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/Derrick_Burke_headshot-465x465.jpeg' class='avatar pp-user-avatar avatar-96 photo ' height='96' width='96'\/>"},{"ID":8992,"user_firstname":"Raymond","user_lastname":"Bunn","nickname":"Ray","user_nicename":"rbunn","display_name":"Raymond Bunn","user_email":"RBunn@esri.com","user_url":"","user_registered":"2019-02-15 17:46:51","user_description":"Ray is an Architect with the Technology and Innovation Team. He leads an initiative to design and test ArcGIS systems with a focus on user experience.","user_avatar":"<img data-del=\"avatar\" src='https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/02\/G73156_RaymondBunn_06-150x150.jpg' class='avatar pp-user-avatar avatar-96 photo ' height='96' width='96'\/>"}],"short_description":"Impact of desktop resources on editors\u2019 productivity and user experience ","flexible_content":[{"acf_fc_layout":"image","image":{"ID":2988102,"id":2988102,"title":"LaptopResources","filename":"LaptopResources.png","filesize":1825828,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/LaptopResources.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis\/administration\/impact-of-desktop-resources-on-editors-productivity-and-user-experience\/laptopresources","alt":"","author":"226402","description":"","caption":"","name":"laptopresources","status":"inherit","uploaded_to":2988030,"date":"2026-11-06 23:32:17","modified":"2026-11-06 23:32:17","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1350,"height":900,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/LaptopResources-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/LaptopResources.png","medium-width":392,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/LaptopResources.png","medium_large-width":768,"medium_large-height":512,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/LaptopResources.png","large-width":1350,"large-height":900,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/LaptopResources.png","1536x1536-width":1350,"1536x1536-height":900,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/LaptopResources.png","2048x2048-width":1350,"2048x2048-height":900,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/LaptopResources-698x465.png","card_image-width":698,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/LaptopResources.png","wide_image-width":1350,"wide_image-height":900}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><i><span data-contrast=\"none\">Are your desktops properly resourced? Have you ever wondered how adding resources might improve your users\u2019 ability to work productivity? Well, we have some test results to show you.<\/span><\/i><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Infrastructure teams naturally look at utilization, capacity, and configuration. Editors experience those choices differently: as a delay while a map redraws, a pause while a tool completes, or a workflow that takes several extra minutes. Repeated across a day and across a team, small delays add up to reduced productivity- which can result in significant operational costs.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">We conducted a <\/span><a href=\"https:\/\/architecture.arcgis.com\/en\/architectures\/test-studies\/desktop-editing-test-study\/introduction.html\"><span data-contrast=\"none\">desktop editing test study<\/span><\/a><span data-contrast=\"none\"> to measure the impact of desktop hardware on user experience and their ability to work efficiently.\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Hardware decisions should begin with the workflows they need to support. In our tests, properly resourcing ArcGIS Pro desktops for our editing workflows produced measurably improved system performance and user productivity.\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">\u00a0The practical lesson is not to default to buying the largest machine. Rather, identify where resources are constrained through testing your own workflows and observing whether additional hardware investment produces a positive ROI when factoring in productivity gains. Let\u2019s dig into the test results to get into the details.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<h3><b><span data-contrast=\"none\">Hardware choices are part of the user experience<\/span><\/b><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;201341983&quot;:0,&quot;335559738&quot;:240,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h3>\n<p><span data-contrast=\"none\">This relationship matters especially in <\/span><a href=\"https:\/\/architecture.arcgis.com\/en\/framework\/architecture-practices\/deployment-concepts\/vdi-and-arcgis-pro.html\"><span data-contrast=\"none\">virtualized environments.<\/span><\/a><span data-contrast=\"none\"> When a GPU is unavailable, <\/span><a href=\"https:\/\/doc.esri.com\/en\/arcgis-pro\/latest\/get-started\/machine-performance-optimization.html\"><span data-contrast=\"none\">ArcGIS Pro<\/span><\/a><span data-contrast=\"none\"> may use CPU emulation for graphics capabilities, which can produce suboptimal performance in workflows that depend on graphics processing. CPU <\/span><a href=\"https:\/\/architecture.arcgis.com\/en\/framework\/architecture-pillars\/performance-and-scalability\/workload-separation.html\"><span data-contrast=\"none\">requirements also vary by workload<\/span><\/a><span data-contrast=\"none\">, making telemetry essential for identifying bottlenecks rather than relying on a generic configuration.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">The hardware choices you make directly affect the user experience and should be evaluated (in large part) by how much they improve or reduce your users\u2019 ability to complete their important business workflows.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<h3><b><span data-contrast=\"none\">No guessing \u2013 let\u2019s look at the results<\/span><\/b><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;201341983&quot;:0,&quot;335559738&quot;:240,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h3>\n<p><span data-contrast=\"none\">Hardware planning often begins with historical sizing, a default recommendation, or the belief that a larger configuration is automatically safer. Those approaches may establish a starting point, but there is no one-size-fits all \u2013 it is going to depend on your specific workflows and requirements.\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">In the <\/span><a href=\"https:\/\/architecture.arcgis.com\/en\/architectures\/test-studies\/desktop-editing-test-study\/introduction.html\"><span data-contrast=\"none\">test study<\/span><\/a><span data-contrast=\"none\"> we used a systematic approach:\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<ol>\n<li><span data-contrast=\"none\">Chose one aspect of the system configuration to test at a time, holding everything else constant:<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span>\n<ul>\n<li><span data-contrast=\"none\">vCPU impact on the ArcGIS Pro desktop instances (4vCPU versus 8vCPU)<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"none\">GPU impact on the ArcGIS Pro desktop instances (with dedicated GPU versus without)<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ul>\n<\/li>\n<li><span data-contrast=\"none\">Tested the same <\/span><a href=\"https:\/\/architecture.arcgis.com\/en\/architectures\/test-studies\/desktop-editing-test-study\/introduction.html\"><span data-contrast=\"none\">workflows<\/span><\/a><span data-contrast=\"none\"> under identical environment conditions for each configuration<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"none\">Captured and collected infrastructure telemetry and user experience<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"none\">Evaluated and compared performance and user efficiency at completing workflows.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ol>\n<p><span data-contrast=\"none\">\u00a0<\/span><span data-contrast=\"none\">All other system hardware and configuration remained constant, so the observed differences could be tied to the configuration change being tested. This includes the workflows \u2013 in this test study, we used foundational Network Information Management <\/span><a href=\"https:\/\/architecture.arcgis.com\/en\/architectures\/network-management\/intended-workflows.html\"><span data-contrast=\"none\">workflows<\/span><\/a><span data-contrast=\"none\"> but the lessons learned apply to any organization.<\/span><\/p>\n<table data-tablestyle=\"MsoTable15Grid4Accent1\" data-tablelook=\"1184\">\n<tbody>\n<tr>\n<td data-celllook=\"4369\"><b><span data-contrast=\"none\">Test<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><b><span data-contrast=\"none\">Purpose<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><b><span data-contrast=\"none\">Configuration one<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><b><span data-contrast=\"none\">Configuration two<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr>\n<td data-celllook=\"0\"><b><span data-contrast=\"none\">GPU<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<td data-celllook=\"0\"><span data-contrast=\"none\">Measure the impact of adding a dedicated GPU to ArcGIS Pro desktop editing workflows.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<td data-celllook=\"0\"><span data-contrast=\"none\">Amazon EC2 R5XL <\/span><span data-contrast=\"none\">(<\/span><span data-contrast=\"none\">without a dedicated GPU<\/span><span data-contrast=\"none\">)<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<td data-celllook=\"0\"><span data-contrast=\"none\">Amazon EC2 G4DNXL with a 16 GB GPU<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr>\n<td data-celllook=\"0\"><b><span data-contrast=\"none\">CPU<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<td data-celllook=\"0\"><span data-contrast=\"none\">Measure the impact of adding 4 vCPU to ArcGIS Pro desktop editing workflows.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<td data-celllook=\"0\"><span data-contrast=\"none\">Amazon EC2 G4DNXL GPU-enabled desktop with 4 vCPU<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<td data-celllook=\"0\"><span data-contrast=\"none\">Amazon EC2 G4DN2XL GPU-enabled desktop with 8 vCPU<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h4><strong>How we measured performance and productivity<\/strong><\/h4>\n<p><span data-contrast=\"none\">To measure the impact of a specific configuration choice, we recorded how long each editor took to complete their workflows alongside the average CPU and memory utilization. This made it possible to compare desktop resource utilization as the editors executed their workflows, step by step.\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">We focused on three simple questions:\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<table data-tablestyle=\"MsoTable15Grid4Accent1\" data-tablelook=\"1184\">\n<tbody>\n<tr>\n<td data-celllook=\"4369\"><b><span data-contrast=\"none\">Questions<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><b><span data-contrast=\"none\">What did we do?<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr>\n<td data-celllook=\"4096\"><b><span data-contrast=\"none\">How was system utilization impacted?<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<td data-celllook=\"16\"><span data-contrast=\"none\">Made one hardware resource adjustment at a time, such as GPU availability or vCPU allocation, while keeping the workflows, data, applications, and backend systems consistent.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr>\n<td data-celllook=\"4096\"><b><span data-contrast=\"none\">Did user productivity improve?<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<td data-celllook=\"16\"><span data-contrast=\"none\">Reran the same workflows and compared workflow completion times of both configurations.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr>\n<td data-celllook=\"4352\"><b><span data-contrast=\"none\">Was the improvement worth the cost?<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<td data-celllook=\"272\"><span data-contrast=\"none\">Compared the measured workflow execution time improvement with the additional infrastructure expense.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><span data-contrast=\"none\">This provides a consistent, repeatable way to test hardware decisions as workloads, software, data volumes, and infrastructure options change.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<h3><b><span data-contrast=\"none\">What the results show us<\/span><\/b><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;201341983&quot;:0,&quot;335559738&quot;:240,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h3>\n<h4><b><span data-contrast=\"none\">Testing impact of GPU-enabled desktops<\/span><\/b><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;201341983&quot;:0,&quot;335559738&quot;:240,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h4>\n<p><span data-contrast=\"none\">One key result from our tests was the effect of GPU-enabled desktops on editor\u2019s productivity. The GPU-enabled desktops reduced workflow execution time by 12% to 23% across the tested workflows, as compared to those without a GPU. Having a dedicated GPU also reduced average CPU utilization by 15% to 44% and average memory use by approximately 15% to 16% in the reported workflow comparisons.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">In the graphs below, you can see the resource utilization across our Remove Service workflow with (below) and without a dedicated GPU (above). You can see the noticeable decrease in CPU utilization when performing that workflow on a GPU-enabled desktop.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2988066,"id":2988066,"title":"removeservice1","filename":"removeservice1.png","filesize":157143,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/removeservice1.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis\/administration\/impact-of-desktop-resources-on-editors-productivity-and-user-experience\/removeservice1","alt":"","author":"226402","description":"","caption":"","name":"removeservice1","status":"inherit","uploaded_to":2988030,"date":"2026-11-06 22:55:21","modified":"2026-11-06 22:55:21","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":904,"height":324,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/removeservice1-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/removeservice1.png","medium-width":464,"medium-height":166,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/removeservice1.png","medium_large-width":768,"medium_large-height":275,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/removeservice1.png","large-width":904,"large-height":324,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/removeservice1.png","1536x1536-width":904,"1536x1536-height":324,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/removeservice1.png","2048x2048-width":904,"2048x2048-height":324,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/removeservice1-826x296.png","card_image-width":826,"card_image-height":296,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/removeservice1.png","wide_image-width":904,"wide_image-height":324}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/architecture.arcgis.com\/assets\/images\/tech-papers\/removeservice1.png"},{"acf_fc_layout":"image","image":{"ID":2988067,"id":2988067,"title":"removeservice2","filename":"removeservice2.png","filesize":591020,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/removeservice2.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis\/administration\/impact-of-desktop-resources-on-editors-productivity-and-user-experience\/removeservice2","alt":"","author":"226402","description":"","caption":"","name":"removeservice2","status":"inherit","uploaded_to":2988030,"date":"2026-11-06 22:55:39","modified":"2026-11-06 22:55:39","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":2071,"height":742,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/removeservice2-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/removeservice2.png","medium-width":464,"medium-height":166,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/removeservice2.png","medium_large-width":768,"medium_large-height":275,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/removeservice2.png","large-width":1920,"large-height":688,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/removeservice2-1536x550.png","1536x1536-width":1536,"1536x1536-height":550,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/removeservice2-2048x734.png","2048x2048-width":2048,"2048x2048-height":734,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/removeservice2-826x296.png","card_image-width":826,"card_image-height":296,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/removeservice2-1920x688.png","wide_image-width":1920,"wide_image-height":688}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/architecture.arcgis.com\/assets\/images\/tech-papers\/removeservice2.png"},{"acf_fc_layout":"content","content":"<p><span data-contrast=\"none\">\u00a0More important tha<\/span><span data-contrast=\"none\">n<\/span><span data-contrast=\"none\"> resource utilization improvements themselves, is the impact on the users\u2019 ability to do their work productively. Measured as workflow completion time, the user-experience impact was clear: when editors did their workflows on a GPU-enabled desktop, they were able to complete them on average 19% faster. <\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">All this to say, every workflow took less time to complete with a GPU-enabled desktop, with time savings ranging from 1 to 3.5 minutes. This might not seem like a lot \u2013 but can have a significant impact on your business operations when you consider the number of users doing many workflows throughout their workday. To dive deeper, check out the test results <\/span><a href=\"https:\/\/architecture.arcgis.com\/en\/architectures\/test-studies\/desktop-editing-test-study\/test-results.html\"><span data-contrast=\"none\">here<\/span><\/a><span data-contrast=\"none\">.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2988068,"id":2988068,"title":"workflowsteps1","filename":"workflowsteps1.png","filesize":93686,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/workflowsteps1.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis\/administration\/impact-of-desktop-resources-on-editors-productivity-and-user-experience\/workflowsteps1","alt":"","author":"226402","description":"","caption":"","name":"workflowsteps1","status":"inherit","uploaded_to":2988030,"date":"2026-11-06 22:55:59","modified":"2026-11-06 22:55:59","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1705,"height":788,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/workflowsteps1-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/workflowsteps1.png","medium-width":464,"medium-height":214,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/workflowsteps1.png","medium_large-width":768,"medium_large-height":355,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/workflowsteps1.png","large-width":1705,"large-height":788,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/workflowsteps1-1536x710.png","1536x1536-width":1536,"1536x1536-height":710,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/workflowsteps1.png","2048x2048-width":1705,"2048x2048-height":788,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/workflowsteps1-826x382.png","card_image-width":826,"card_image-height":382,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/workflowsteps1.png","wide_image-width":1705,"wide_image-height":788}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/architecture.arcgis.com\/assets\/images\/tech-papers\/workflowsteps1.png"},{"acf_fc_layout":"content","content":"<p><span data-contrast=\"none\">The improvement was not isolated to one operation. As you can see in the chart above, most measured workflow steps completed faster with the GPU-enabled desktop (orange), although the size of the difference varied by step.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<h4><b><span data-contrast=\"none\">Testing impact of increased vCPU on desktops<\/span><\/b><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;201341983&quot;:0,&quot;335559738&quot;:240,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h4>\n<p><span data-contrast=\"none\">The vCPU tests examined whether additional processing capacity could improve desktop editing performance. It compared configurations with 4 vCPU and 8 vCPU using ArcGIS Pro 2.9.5. Workflows were run under load, with the surrounding system hardware and configuration held constant so the desktop configurations could be compared.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">As with the GPU test, the result that matters most to editors is how long it takes to finish their work. Increasing the desktop allocation from 4 to 8 vCPU reduced workflow execution time by 10% overall. However, as you can see in the chart below, the additional vCPU did not accelerate every workflow step equally.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2988070,"id":2988070,"title":"workflowsteps2cpu","filename":"workflowsteps2cpu.png","filesize":86706,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/workflowsteps2cpu.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis\/administration\/impact-of-desktop-resources-on-editors-productivity-and-user-experience\/workflowsteps2cpu","alt":"","author":"226402","description":"","caption":"","name":"workflowsteps2cpu","status":"inherit","uploaded_to":2988030,"date":"2026-11-06 22:56:15","modified":"2026-11-06 22:56:15","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":2149,"height":568,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/workflowsteps2cpu-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/workflowsteps2cpu.png","medium-width":464,"medium-height":123,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/workflowsteps2cpu.png","medium_large-width":768,"medium_large-height":203,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/workflowsteps2cpu.png","large-width":1920,"large-height":507,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/workflowsteps2cpu-1536x406.png","1536x1536-width":1536,"1536x1536-height":406,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/workflowsteps2cpu-2048x541.png","2048x2048-width":2048,"2048x2048-height":541,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/workflowsteps2cpu-826x218.png","card_image-width":826,"card_image-height":218,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/workflowsteps2cpu-1920x507.png","wide_image-width":1920,"wide_image-height":507}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/architecture.arcgis.com\/assets\/images\/tech-papers\/workflowsteps2cpu.png"},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW187617888 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW187617888 BCX0\">The larger <\/span><span class=\"NormalTextRun SCXW187617888 BCX0\">configuration <\/span><span class=\"NormalTextRun SCXW187617888 BCX0\">also lowered average CPU <\/span><span class=\"NormalTextRun SCXW187617888 BCX0\">utilization<\/span><span class=\"NormalTextRun SCXW187617888 BCX0\"> by <\/span><span class=\"NormalTextRun SCXW187617888 BCX0\">approx<\/span><span class=\"NormalTextRun SCXW187617888 BCX0\">imately <\/span><span class=\"NormalTextRun SCXW187617888 BCX0\">63%, from about 41% to 48% with 4vCPU to <\/span><span class=\"NormalTextRun SCXW187617888 BCX0\">15% to 19% <\/span><span class=\"NormalTextRun SCXW187617888 BCX0\">utilization<\/span><span class=\"NormalTextRun SCXW187617888 BCX0\"> with 8vCPU<\/span> <span class=\"NormalTextRun SCXW187617888 BCX0\">(below).<\/span><\/span><span class=\"EOP Selected SCXW187617888 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2988060,"id":2988060,"title":"AverageCPU (1) (1)","filename":"AverageCPU-1-1.png","filesize":114010,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/AverageCPU-1-1.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis\/administration\/impact-of-desktop-resources-on-editors-productivity-and-user-experience\/averagecpu-1-1","alt":"","author":"226402","description":"","caption":"","name":"averagecpu-1-1","status":"inherit","uploaded_to":2988030,"date":"2026-11-06 22:51:39","modified":"2026-11-06 22:51:39","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":2245,"height":728,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/AverageCPU-1-1-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/AverageCPU-1-1.png","medium-width":464,"medium-height":150,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/AverageCPU-1-1.png","medium_large-width":768,"medium_large-height":249,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/AverageCPU-1-1.png","large-width":1920,"large-height":623,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/AverageCPU-1-1-1536x498.png","1536x1536-width":1536,"1536x1536-height":498,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/AverageCPU-1-1-2048x664.png","2048x2048-width":2048,"2048x2048-height":664,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/AverageCPU-1-1-826x268.png","card_image-width":826,"card_image-height":268,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/AverageCPU-1-1-1920x623.png","wide_image-width":1920,"wide_image-height":623}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span data-contrast=\"none\">This indicates that the additional processing capacity helped ArcGIS Pro complete the overall workloads more efficiently. The practical lesson is to evaluate additional vCPU by the workflow time it saves and determine whether the resulting productivity gains justify the added infrastructure cost. To dive deeper, check out the <\/span><a href=\"https:\/\/architecture.arcgis.com\/en\/architectures\/test-studies\/desktop-editing-test-study\/test-results.html\"><span data-contrast=\"none\">CPU test results<\/span><\/a><span data-contrast=\"none\">.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<h3><b><span data-contrast=\"none\">Conclusion: Your users notice seconds<\/span><\/b><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;201341983&quot;:0,&quot;335559738&quot;:240,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h3>\n<p><span data-contrast=\"none\">The results point to three practical conclusions:<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"18\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Evaluate hardware choices by their impact on your workflows: this shows whether a configuration change helps users complete real work more efficiently, not just whether resource utilization improves.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"18\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"2\" data-aria-level=\"1\"><span data-contrast=\"none\">Measuring workflow durations across the tests helps us measure user productivity and operational cost savings: saving 1.2 to 3.2 minutes per workflow is meaningful when repeated across users and workdays<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"18\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;multilevel&quot;}\" data-aria-posinset=\"3\" data-aria-level=\"1\"><span data-contrast=\"none\">While the exact impact of a GPU or the amount of vCPU will vary based on your system and workflows, both sets of tests show the importance of properly resourced ArcGIS Pro desktops for system performance and end user productivity.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"none\">The best configuration is the one that supports real work, improves user experience, and delivers value that justifies its cost.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Start by testing your own representative workflows. Measure the current experience, then compare whether additional CPU capacity or a dedicated GPU produces enough improvement in workflow time and responsiveness to justify the investment.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<h3><b><span data-contrast=\"none\">How you can get started<\/span><\/b><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;201341983&quot;:0,&quot;335559738&quot;:240,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h3>\n<ol>\n<li><span data-contrast=\"none\">Choose one important ArcGIS Pro workflow and capture how long it typically takes as well as resource utilization.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"none\">Test one proposed GPU or CPU change under the same conditions.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"none\">Compare workflow time, telemetry, and observed responsiveness.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"none\">Translate measured time savings into staff productivity and compare that value with the added infrastructure cost.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"none\">Document the result and repeat the test as workloads and infrastructure change.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:100,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ol>\n"}],"related_articles":"","show_article_image":false,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/LaptopResources_826x465.png","wide_image":false},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>ArcGIS Pro vCPU and GPU Performance: Desktop Resources<\/title>\n<meta name=\"description\" content=\"Learn how vCPU and GPU resources affect ArcGIS Pro performance, editing workflows, user experience, and productivity.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis\/administration\/impact-of-desktop-resources-on-editors-productivity-and-user-experience\" \/>\n<meta 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