For Graphistry's GPU platform, we suggest our users to go with server-grade GPUs because they get (a) more memory and (b) great multitenancy. So using MapD as a personal system is an expensive use of resources, but when a system is architected and billed as an elastic, multitenant system, total cost of ownership for a team is less. Not all platforms are built for this (and I don't know enough about MapD vs. other GPU databases), but that's the engineering view.
And mini-disclaimer: Graphistry is a related platform focused on scaling & automating investigations. Part of that is a GPU compute stack the we started building around the same time as MapD, though we're not in the database business. E.g., our customers will generally use us to look across multiple other systems that already feature high-availability, long-term storage, and scaleout querying for TB+ storage. As some examples: SQL engines, Spark, Splunk, Datastax, and various graph databases.
And mini-disclaimer: Graphistry is a related platform focused on scaling & automating investigations. Part of that is a GPU compute stack the we started building around the same time as MapD, though we're not in the database business. E.g., our customers will generally use us to look across multiple other systems that already feature high-availability, long-term storage, and scaleout querying for TB+ storage. As some examples: SQL engines, Spark, Splunk, Datastax, and various graph databases.