ARVICA / DOCUMENTATION
GPU and instance selection
Select memory and topology for the workload you will run.
Memory before model name
Allow room for model weights, activations, runtime caches and batch size. Increasing the GPU count does not automatically create one shared memory pool. Check that your framework supports the intended parallel execution.
GPU, host and rack
Compare both the accelerator and host configuration. CPU architecture, RAM, local disk, PCIe or SXM packaging and interconnect matter. GB200/GB300 rack-scale configurations require a defined system scope rather than a single-card comparison.
VM and bare metal
Confirm whether the offered instance is a VM or bare metal and what administrator access is included. Do not infer root access, NVLink, InfiniBand or a particular host specification from a GPU family name alone.