Bringing Browser-Based MFA SSO to the OpenStack CLI
Learn how a lightweight keystoneauth1 plugin brings your existing browser-based MFA and SSO to the OpenStack CLI, with no changes to any client tools.
Perspectives, mises à jour et histoires de notre équipe
Learn how a lightweight keystoneauth1 plugin brings your existing browser-based MFA and SSO to the OpenStack CLI, with no changes to any client tools.
Hyperscaler AI looks fast but hides long-term lock-in and rising costs. See how OpenStack and Kubernetes deliver GPU infrastructure you actually control.
Many AI clusters run at only 30–50% GPU utilization. Learn why GPUs sit idle and how Kubernetes, scheduling, and better infrastructure design can improve AI infrastructure efficiency.
The use of GPUs in data center environments brought about a revolution in data processing for enterprises. How was this possible? Here is a look.
The use of GPUs in data center environments brought about a revolution in data processing and analysis for many enterprises. We saw a technology created for a specific purpose being turned into something more with GPUs, benefitting the entire world with it. How is this possible? Why are GPUs ideal for tasks within data center environments? Here is a look.
Graphics Processing Units (GPUs) came to the tech scene as a means to ease and improve the visual display aspect of computers; however, a few decades later, they have contributed to much more than graphics. GPUs can perform complex computations exponentially better than Central Processing Units (CPUs).
GPUs were first developed for the gaming industry - to process algorithms and render 3D images on 2D displays. Conventional CPUs weren't really capable of achieving this effectively. Later, it was identified that GPUs could be used to perform double the tasks or deliver calculations in a fraction of the time compared to CPUs. Hence, the purpose and usage of GPUs diversified. As the need for computations and better processing rose in server environments, GPUs in data centers became normal.
As mentioned, due to their highly parallel structure, GPUs are capable of processing hundreds of thousands of small programs at once. Modern GPUs use caches, register files, frame-buffers, and other hardware that increase their capabilities and performance. These variances enable companies to make use of GPUs in ways that suit their particular processing needs. The only thing enterprises need to make effective use of GPUs in data centers in developing and implementing the code necessary for it to work.
The advantage of being able to use GPUs and access data quickly without noticeable stalling between tasks can have proven invaluable for those in the field of machine learning, deep learning, AI, data mining, etc. In data centers, GPUs have proven their worth as workload accelerators as well - Virtual Desktop Infrastructure (VDI) is a noteworthy example.
With the evolution of GPUs and their adoption in data centers, it is now possible to process hundreds of thousands of minuscule programs simultaneously. When it comes to harnessing the power of enterprise-grade GPUs, it is evident that they have the ability to impact your business substantially.
We at VEXXHOST use AMD & NVIDIA GPUs to power our clouds and can support a diverse cloud workload to suit a wide variety of needs. Additionally, our cloud services are based on OpenStack, making the environments free from licensing fees or vendor lock-ins. For many of our clients, private clouds are the preferred choice because of their highly scalable and secure nature.
Speaking of private clouds, you can now run on a fully agile and customized cloud from VEXXHOST, with no licensing fees and smooth 2-week migration. In fact, we're ready to put our money where our mouth is. We're so confident in being able to save you at least 20% or more on your current cloud infrastructure expenditure that if proven wrong- we'll give you $1,000 credit to our public cloud.
Excited? Find out more.
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