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.
Equip your data center to better manage heavy workloads from AI/ML/DL. Optimize GPU performance and pick the right networking system to prepare.
Earlier, we have talked about the extensive and consuming demands of AI/ML/DL. We went on to discuss how your data center can prepare to accommodate such workloads in its environment. Here are some more ways in which your data center can take on AI and its subsets and their operations:
AI/ML/DL applications are compute-intensive. Therefore, GPUs are the preferred resources for all processing related to these applications. However, training data sets mostly except RAM, making a large number of files hard to store and manage. Hence, strategizing a balance between GPU and CPU power, along with memory and network bandwidth for both GPU servers and infrastructure storage, is critical for an efficient data center.
Back-fitting new workloads like AI/ML/DL into the existing network infrastructure is poor practice. The current infrastructure is unlikely to support complex computations of that nature. For fast and efficient data delivery, AI/ML/DL require low latency, high bandwidth, smart offloads, and high message rate. Therefore, picking the right network transport becomes vital to efficiency.
The storage system for AI/ML/DL is significantly faster than others in a data center. Consequently, recovering it from backup after a complete failure takes longer and disrupts ongoing operations. The read-mostly nature of DL training makes it a good fit for distributed erasure coding where the highest level of fault tolerance is already built into the primary storage system, with a minimal difference between raw and usable capacity.
Accommodate any size or type of drive, so that as flash media evolve and flash drive characteristics expand, data centers can maximize price/performance at scale, when it matters the most.
AI data sets need to grow over time to improve the accuracy models further. So, storage infrastructure should achieve a close-to-linear scaling factor, where each incremental storage addition brings the equivalent incremental performance. This allows organizations to start small and grow non-disruptively as business dictates.
When it comes down to storing and accessing large amounts of data securely through your business or organization, cloud services may be the solution that you're looking for. VEXXHOST has two data center regions within Quebec for high-density power exactly where you want it - in Canada. We can also give you high-speed direct access to Silicon Valley's Tier-1 carriers and blazing connectivity through our Santa Clara public cloud region.
If you are interested in knowing more about our data center specs or are interested in a public or hosted private cloud environment, get in touch!
Choose from Atmosphere Cloud, Hosted, or On-Premise.
Simplify your cloud operations with our intuitive dashboard.
Run it yourself, tap our expert support, or opt for full remote operations.
Leverage Terraform, Ansible or APIs directly powered by OpenStack & Kubernetes