GPU in the cloud is becoming an interesting marketplace for increasing workloads in the GPU sector.
We’ve noticed an increase in requests over time as GPU cloud providers, GPU users and large GPU-intensive environments become more popular.
There are lots of things to consider when it comes to building out GPU clouds and GPU hardware.
First of all, the GPU(s). There are many GPUs out there and many different lines of GPU. With the demand for GPUs right now, it is also about making sure you understand which set of GPUs will work for your workloads as many particular GPUs are not available. NVIDIA has a wider range of consumer & pro-grade cards and picking the right ones for the right workloads is important. Consumer cards typically come under the “GeForce” tag. We’ll be happy to help depending on your workloads to suggest the right cards for you. We are an NVIDIA Preferred Solution Provider.
The next part element is the CPU and the elements that relate to CPU & GPU interaction. This is mostly related to PCI-Express workloads. Intel and AMD, especially with the emergence of the Intel Alder Lake CPUs and the upcoming Zen 4 based AMD CPUs, this is ever-increasing and more compatible than ever. We’d be more than happy to help you through this process. We are a Titanum partner of Intel and an Elite partner of AMD.
The other areas we consider priority when building out which a power-heavy and space-heavy environment are a few other things. Chassis GPU density, power supplies, RAM, hard drives and other elements outside-the-chassis like network depending on your workload types.
Enquire today about our GPU Cloud solutions and GPU Servers, by using the form below, and one of our expert team will be. in touch.
General Enquiry Form
More from our blog
Optimizing your GPU Private Cloud
Building a GPU cloud can be a timely and expensive experience. We discuss our recommended options for software and hardware for an optimal GPU cloud.
Which GPU is best for me?
There are lots of GPU choices depending on your use case and use cases vary wildly. Whether you are a home user gaming or a datacenter trying to machine learn 24/7, the use cases vary.
What is a GPU server?
GPUs face complex algorithm problems that equate to use cases we know such as AI, machine learning, 3D rendering and more. A CPU may be used for general computation but a GPU, with lots of cores compared to a CPU, is focused on specific tasks and as such, can perform them at a much faster rate.