Through various recent projects, I had to work through the clutter of information regarding NVIDIA vGPU licensing.
Here is a small summary of this information.
NVIDIA vGPU Architecture
Under the control of the NVIDIA GPU Virtual Manager, running in the hypervisor, the NVIDIA Physical GPU can operate multiple virtual GPU devices (vGPUs), that can be assigned directly to the Guest VM.
The Guest VMs use the NVIDIA virtual vGPUs in the same way as a physical GPU would come from the hypervisor by direct passed through. The NVIDIA Driver loaded into the guest VM provides Direct GPU Access for high-performance operations. The NVIDIA Virtual GPU Manager paravirtualized interface performs the non-performance management operations for the NVIDIA Driver.
Delivery Groups: New Studio interface for creating machine restart schedules
In earlier releases, you used Studio to create a restart schedule for machines in a Delivery Group. To create multiple schedules, you used PowerShell cmdlets. Now, the updated Studio interface enables you to create and manage one or more restart schedules.
A schedule can affect either:
All of the machines in the group.
One or more (but not all) machines in the group. The machines are identified by a tag that you apply to the machine. This is called a tag restriction, because the tag restricts an action to only items (in this case, machines) that have the tag.
The Maintenance Plan Wizard creates jobs for the Microsoft SQL Server Agent. This allows you to perform various database management tasks at specific intervals, e.g. Backups, database health checks or database statistic updates. This should be configured for all SQL databases, as this will prevent the transaction logs from becoming excessively large. Excessively large transaction logs can make the server inefficient and unstable.
The main task of every CPU is to process data. But here is the misconception, the faster the CPU (or the more CPUs I allocate), the faster the data will be processed. This is unfortunately not quite that easy, because before the CPU can process the data, it must be read out by the slower system RAM and that latency can slow the CPU processing. In order to minimize the time the CPU is waiting on reading data, CPU architectures include on-chip memory caches (local RAM) that are much faster than RAM (the access is up to 95% faster).