ComputingRecent research from on this topic, conducted in collaboration with Intel®, shows that hardware specifications are of great value to IT managers in all industries. Around 60% of survey respondents agree that hardware is critical to efficient AI and analytics workflows, with a third strongly agreeing.
IT decision makers recognize that efficiency and advanced analytics capabilities depend on memory management and parallelizable computing power.
The right infrastructure is therefore essential to allow access to this data in a way that allows queries and actions to be performed. Whether workloads are running on-premises or in the cloud, memory, compute, and networking infrastructure must be designed to complement the design and deployment of AI solutions.
Hardware has a direct impact on this performance and throughput speed, which means that an optimized infrastructure will ensure that AI implementations do not act as a bottleneck on business operations, but speed them up at the same time. square.
lean and green
Developing an IT architecture that operates efficiently, with low power consumption is a growing concern. Data center energy and water consumption are coming under increasing scrutiny as organizations grapple with rising costs and increased regulatory pressure over environmental concerns.
It is increasingly important to review carbon footprints and invest in sustainable infrastructure. Inefficient processors or supporting hardware platforms will not only create organizational productivity issues, but will further impede innovation for processes like AI.
Companies should look to invest in hardware designed and optimized for AI workloads. To take full advantage of this, it is important to determine where AI technologies can be used specifically for their organization, based on business outcomes.
Leveraging the right technology and building digital initiatives on reliable infrastructure are key success factors. AI is performance driven and any system is only as fast as the slowest component. Without capable hardware in place, you fall at the first hurdle.
To learn more about Computingresearching real-world AI use cases with real-world results, read the full report.
Sponsor Information – Intel
Organizations need to harness AI to extract value from data, but the challenges abound. Data pre-processing, from discovery and breaking down silos, to quality control and its management from edge to cloud, comes first. Taking the right approach to modeling, from analytics to machine or deep learning, with the right technology and expertise, comes next.
Intel offers a holistic and open path, addressing the entire data, modeling, and deployment pipeline, with the freedom to compute on the architecture that works best, including the single x86 processor with built-in AI acceleration .