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Compute

Artificial Intelligence

Artificial intelligence

Corporations are already making significant investments in AI-related technologies. The artificial Intelligence technologies and the infrastructure to support them have become more affordable and mature.

This forces corporations and IT executives to have an opinion and understand how to adopt and apply the technologies to benefit from the many new possibilities.

As AI goes from experimental (POC) towards adoption, the need for computing resources and infrastructure costs will undoubtedly increase. Overheads will increase over time, as technology becomes more complex and resource demanding.

 

Top priorities when choosing architecture

  • Infrastructure Capacity: To take advantages of AI, organizations need sufficient performance-based resources, including CPUs for basic AI workloads, and with GPUs for more advanced workloads (deep learning).
  • Storage Scalability: It is fundamental that your infrastructure has the ability to scale storage as the volume of data grows. Understating corporations AI roadmaps and need for real-time decisions has a huge impact on choice of architecture.
  • Cost-effective: As AI models become more complex, they become more expensive to run, so getting extra performance from your infrastructure is pivotal to containing costs

 

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When you choose the right architecture it allows you to benefit from the many future abilities of artificial intelligence, with cost-effective dedicated servers tailored for your present and future business needs. This will enable the ability to continue the investments on AI without an increase in budgets over time.

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