technologies. AI workloads comprise two main components - inferencing and training. IT departments can run AI algorithm development and training, which are performance intensive, on premises, IDC says. And the data is trending that way, as 55 percent of IT decision makers
cited performance as the main reason for running GenAI workloads on premises. Conversely, less intensive inferencing tasks can be run in a distributed fashion at edge locations, in public cloud environments or on premises.high-performance computing applications ALSO comprise two major components - modeling and simulation. And like AI workloads, HPC model development can be performance intensive, so it may make sense to run such workloads on premises where there is lower risk of latency.
One caveat for performance-heavy workloads that IT leaders should consider: Specialized hardware such asis expensive. As a result, many organizations may elect to run AI and HPC workloads in resource-rich public clouds. However, running such workloads in production can cause costs to soar, especially as the data grows and the attending gravity increases. Moreover, repatriating an AI or HPC workload whose data grew 100x while running in a public cloud is harsh on your IT budget.
Also consider: Workload placement is not a one-and-done activity. Repatriating workloads from various clouds or other environments to better meet the business needs is always an option.portfolio of solutions accounts for the various workload placement requirements and challenges your organization may encounter as you build out your multicloud estate. Dell APEX' subscription consumption model helps you procure more computing and storage as needed - so you can reduce your capital outlay.
It's true: The stakes for assembling a jigsaw puzzle aren't the same as allocating workloads in a complex IT environment. Yet completing both can provide a strong feeling of accomplishment. How will you build your multicloud estate?Brought to you by Dell Technologies.