A successful, cost-effective AI journey is built on high performance hardware. Analysing vast amounts of data quickly and accurately, a key mechanism in AI, relies on specialist technology with capable compute features. Including highly threaded workloads that demand numerous cores, high bandwidth memory, and AI-specific instruction sets – all while keeping a lid on power demands.
Computing's recent research into this topic, conducted in collaboration with Intel®, finds hardware specification is of high value to IT leaders across industries. Around 60 per cent of survey respondents agree hardware is vital to effective AI and analytics workflows with a third strongly agreeing.
IT decision-makers recognise that efficiency and advanced analytics capabilities depend on memory management and parallelisable computer power.
The right infrastructure is therefore paramount in enabling access to that data in a way that allows queries and actions to be carried out. Whether workloads are carried out on premises or in the cloud, memory, compute, and networking infrastructure must be architected to complement the design and deployment of AI solutions.
Hardware directly impacts that speed of performance and throughput, meaning optimised infrastructure will ensure AI implementations don't act as a bottleneck on business operations, and instead accelerate them.
Lean and green
The development of computing architecture that runs efficiently, with low power consumption is of increasing concern. Data centre energy and water use is under increasing scrutiny as organisations grapple with mounting costs and greater regulatory pressure in the face of environmental concerns.
It is all ever more important to review carbon footprints and invest in sustainable infrastructure. Inefficient processors or supporting hardware platforms will not only create problems for organisational productivity but further prevent innovation for processes such as AI.
Companies should look to invest in hardware that has been designed and optimised for AI workloads. To reap its full value, it is important to determine where AI technologies can be used for their organisation specifically, based on business outcomes.
Leveraging the right technology and building digital initiatives on dependable infrastructure are key drivers of success. AI is performance oriented, and any system is only as fast as the slowest component. Without capable hardware in place, you're falling at the first hurdle.
To learn more about Computing's research into real world AI use cases with real results, read the full report.
Sponsor insight - Intel
Organisations must harness AI to extract value from data, but challenges abound. Data pre-processing, from discovery to breaking down silos, to quality control, and managing it from edge to cloud, come first. Taking the right approach to modelling, from analytics to machine or deep learning, with the right technology and expertise comes next.
Intel provides a holistic and open path forward, addressing the full data, modelling and deployment pipeline, with the freedom to compute on whichever architecture is best, including the only x86 CPU with built-in AI acceleration.
