Dragonfly Helpdesk

In general, you should choose a workstation-class (Nvidia Quadro or ATI FirePro) or a gamer-class (Nvidia GeForce or ATI Radeon) graphics card. 

If you want to use Deep Learning with Dragonfly, you can only choose Nvidia cards with CUDA support. Please see this page for more information: https://helpdesk.theobjects.com/a/solutions/articles/48001140696 

While Dragonfly performance will improve with more video RAM (VRAM) and more GPU processing capability (GPU cores), there is no certain answer for which card is sufficient for you, as it depends on the size of your data files and what you are going to do with those files. For many users of micro-CT and 3D-EM, a card with 4G VRAM is about the minimum requirement to get the work started, and 8G VRAM or more is highly recommended. 

Below are the highest-performance graphics cards to consider, ranked from highest to lower performance (last updated February 2019), where the last four-digit number in each description, for example "4608", indicates the number of CUDA parallel-processing cores. 

For the same amount of VRAM and GPU capability, the GeForce cards are usually lower priced than the Quadro cards. For example, the Nvidia GeForce RTX 2070 8GB  card costs about 500$ (as of May 2020), making it a good choice for low budget.

When building a system yourself, you must confirm that your system is compatible with your preferred graphics card. The constraints are the interface of the motherboard (e.g. PCIe 3.0) and the power capacity of the power-supply unit (e.g. 800 W).

 

For more information: http://www.theobjects.com/dragonfly/system-requirements.html