Provision GPU Resources
Provision GPU Resources
Not every workload wants to run on a CPU. When graphics, rendering, or parallel math become the bottleneck, offloading to a GPU co-processor is usually the fix — the question is whether that GPU is shared or dedicated.
Provision GPU Resources
Workloads can sometimes be offloaded from the CPU and passed to other co-processors or hardware accelerators. A common solution for this offloading is the Graphics Processing Unit (GPU). AWS, Azure, and GCP all use NVIDIA GPUs for this purpose.
These processors benefit VMs running specific kinds of work. Tap a use case:
Shared vGPUs vs. Pass-Through GPUs
Like CPUs, GPUs are exposed to VMs as available hardware — a Virtual GPU, or vGPU. But there are two ways to hand that hardware out: sliced across many VMs, or dedicated entirely to one.
Preconfigured GPU Instances
Just like memory- or storage-optimized instances, cloud administrators select GPU-capable configurations from what the provider already offers — rather than assembling one from scratch.
NVIDIA on the AWS Marketplace
NVIDIA offers preconfigured graphics-intensive VM workstation instances directly on the AWS Marketplace. For example, an administrator could select an Ubuntu Linux platform with an NVIDIA GPU configured specifically for cloud-based gaming — no manual GPU driver or graphics stack setup required.
Manage Clock Speed
Clock speed is a common performance measurement for CPUs. Tuning it for the workload — a computationally intensive science rendering program, for instance — can buy real time efficiency. As high-performance computing workloads scale up, the impact on CPU performance grows with them.