RTX 5 Series

renting rtx 5 series gpus (5090/5080/5070/5060) many of our recommended templates now support blackwell series nvidia gpu's including the rtx 5 series blackwell gpus do not have the same backwards compatibility as seen in some previous generation nvidia gpu's so it is important that the template and docker image has been configured to use cuda 12 8 and pytorch 2 7 or greater any template that is known to be compatible with this gpu type will automatically show these gpus in the offer listing those without support will exclude the unsupported cards when searching for an instance templates configured with the \[automatic] tag will pull the most recent & supported docker image this enables wider support across the range of gpus you can find at vast ai steps to rent an rtx 5000 series gpu on vast ai create / log in to your vast ai account go to cloud v ast ai https //vast ai and either create a new account or log in select a recommended template with "\[automatic]" set as the version tag (this is the default option) to check this, click the 'pencil' icon on the template card to open the template editor, you can view the image tag select the 5 series gpu from search filters in the gpu drop down menu select the specific 5 series card you want to rent or select the whole category review and customize set your storage and further refine your search filters (e g , secure cloud, location, system ram, cpu, etc ) ⚠️ d o not change the docker image because you need to maintain cuda 12 8 and the dev version of pytorch if you switch to an incompatible docker image, you may lose 5 series compatibility select and rent click “rent” next to your preferred server you can now launch jupyter notebooks, ssh into the instance, or start your own training jobs using the pre installed cuda 12 8 / pytorch dev environment tips and troubleshooting check cuda version if you manually change the docker image, ensure it’s compiled for cuda 12 8 or else you may lose compatibility with these gpus stay up to date new pytorch releases (especially nightlies / dev builds) often update their cuda support if you need a stable release, confirm that the docker image tags match a stable version with cuda 12 8 use custom docker if you have your own docker image, you must ensure it is built with cuda 12 8 (and ideally tested on a gpu supporting that version)