You can run jobs at different sizes and durations on DeltaAI. The following lists the different queues that you can submit to, describing how many nodes you get, how long you can run, the type of resources you get, and the average wait time.
Jobs are submitted through the Slurm scheduler. Run batch jobs with sbatch, launch interactive work with srun or salloc, or access compute resources through Open OnDemand applications such as JupyterLab.
Node sharing is the default behavior. The smallest allocatable unit is a single GH200 superchip - roughly one GPU, 72 CPU cores, and a quarter of the node's memory - so jobs do not need to request a whole node. Users who need exclusive access can request all consumable resources for the node, or use Slurm options such as --exclusive --mem=0. All nodes are of a single type and any job can span any node.
Jobs are charged on the resources reserved, not the resources used, and are charged in GPU Hours. The charge is sized by the number of superchips needed to satisfy whichever of your GPU, CPU-core or memory request is largest, so asking for extra memory or cores alongside one GPU can raise the charge. The ghx4-interactive queue is charged at a higher rate than ghx4, takes at most 4 nodes per job, and limits each user to 8 node-hours of running jobs, so use it for development and testing rather than production runs. See [DeltaAI Job Accounting] for current rates.
DeltaAI's CPUs are ARM (aarch64), not x86. Binaries compiled for x86, including binaries built on Delta, will not run here - rebuild for aarch64 or use an ARM-native container. Containers run under Apptainer, not Docker, and images must be linux/arm64.
If you do not specify them, jobs default to a 30-minute wall-clock limit and 1000 MB of memory per core. Jobs are not automatically requeued or restarted by default; add --requeue to enable it.
For more information about running jobs on DeltaAI, see [DeltaAI Running Jobs].