What are the infrastructure requirements for Artificial Intelligence?

In the past few years, I have been asked several times about how Leaseweb positions itself towards Artificial Intelligence (AI). As Leaseweb is mainly focusing on delivering fast and reliable Infrastructure as a Service, we’re not offering AI services directly to our customers.   

Having said that, we work with many customers who deliver AI services to their end user, which means that Leaseweb is used as the foundation of many AI solutions around the globe. For example, our customer Linkfluence uses AI to analyze extensive collections of social data to provide insights for leading brands and agencies. We deployed high-performance dedicated servers to cope with their processor-intensive workloads cost-effectively.  

For this reason, Leaseweb needs to be quite aware of how companies are using Artificial Intelligence and their specific requirements in terms of infrastructure. 

The diffusion of AI 

Businesses and IT executives are already making significant investments in AI-related technologies. Artificial Intelligence is changing everything, and as it becomes more prevalent, organizations will be forced to come to grips with it on a macro level as it changes entire industries, and on a micro level as it impacts business strategy within their ranks. With such consequential change happening at such a brisk pace, some key aspects of AI are worth keeping an eye on as it becomes more pervasive and organizations face a new world of processes and requirements.   

Bughin and Zeebroeck caution that these are still the early days of AI. They estimate that about 35% of companies are either implementing or piloting AI. Still, the impact on markets is already being felt. The disruptive and more efficient business models within this segment may already be “depressing industry margins.” At the same time, these early AI adopters are already moving onto the second wave of AI, which will likely keep them ahead of their competitors for some time to come. 

Infrastructure requirements for AI

From an infrastructure perspective, one thing is clear. As AI moves beyond experimentation toward adoption, it will demand significant computing resources and infrastructure costs. Overheads will snowball as the technology becomes more complex and resource-demanding, and in a world increasingly impacted by AI, finding cost-effective environments to run the intensive processes will be both a requirement and a competitive advantage.   

Businesses will have to adapt and be flexible, especially with regard to infrastructure. Cloud technologies, particularly hybrid cloud solutions, are and will be the foundation of AI as its needs for substantial amounts of data ratchet up. Hybrid cloud solutions will ensure that the needs of businesses and workloads match technology to the demands increasingly required to sustain AI, but not only that, it will also make sure this is at the right cost level.   

Therefore, the biggest question for organizations is: what infrastructure allows for the continual use, development, and implementation of Artificial Intelligence without sacrificing performance?   

Here are five things to keep in mind when evaluating potential partners to ensure choosing the best platform possible.  

1. High computing capacity

To fully take advantage of the opportunities presented by AI, organizations need sufficient performance computing resources, including CPUs and GPUs. A CPU-based environment can handle basic AI workloads, but deep learning involves multiple large data sets and deploying scalable neural network algorithms. For that, CPU-based computing might not be sufficient. For example, GPUs can accelerate deep learning by 100 times compared to traditional CPUs. Computing capacity and density will also grow, as will demand for high-performance networks and storage.

2. Storage capacity

It’s fundamental that your infrastructure has the ability to scale storage as the volume of data grows. Figuring out what kind of storage an organization needs depends on many factors, including the level of AI an organization plans to use and whether they need to make real-time decisions. For example, a FinTech company that uses AI systems for real-time trading decisions may need fast all-flash storage technology, while for other companies slower but very large storage will be the most suitable solution. Businesses need to factor in how much AI data applications will generate. AI applications make better decisions when they’re exposed to more data. As databases grow over time, companies need to monitor capacity and plan for expansion.   

3. Networking infrastructure

Networking is another key component of AI infrastructure. Deep learning algorithms are highly dependent on communications, and networks will need to keep stride with demand as AI efforts expand. That’s why scalability must be a high priority, and that will require a high-bandwidth, low-latency network. The best choice for expansive service is a global infrastructure provider who can ensure the service wrap and technology stack are consistent in all regions.

4. Security

AI can involve handling sensitive data such as patient records, financial information, and personal data. Having this data breached will be a disaster for any organization. Also, the infusion of bad data could cause the AI system to make incorrect inferences, leading to flawed decisions. The AI infrastructure must be secured from end to end with state-of-the-art technology. 

5. Cost-effective solutions

As AI models become more complex, they become more expensive to run, so getting extra performance from your infrastructure is pivotal to corralling costs. Over the next few years, we can expect continued growth in companies using AI, placing heavier burdens on the network, servers, and storage infrastructures to enable the use of this technology.   

By making careful choices and identifying providers who can offer cost-effective dedicated servers, there is an opportunity to boost performance. This will enable companies to continue investing in AI without an increase in budget. 

AI and Leaseweb

Although Leaseweb itself is not currently offering AI services, we are able to deliver the infrastructure that is required for normal workloads and provide a direct connection to the Public Cloud where the Artificial Intelligence services are running. This means that Leaseweb can be part of your journey into the AI world 

  1. Swati Thakur
    Swati Thakur
    March 1, 2022 at 6:53

    Hello my name is Swati Thakur, I have found your blog very impressive and attractive as Artificial intelligence is such an interesting topic. Thank you for sharing such an informative and helpful blog on the internet.

  2. Marcin Kordowski
    Marcin Kordowski
    December 16, 2022 at 10:38

    Very interesting article, is any other case study on you blog?

  3. Jobin Reddy
    Jobin Reddy
    February 9, 2023 at 6:07

    This is a great article highlighting the critical infrastructure requirements for Artificial Intelligence. It’s important to note that AI implementation requires not only powerful hardware but also efficient data storage and management systems. As AI continues to grow and evolve, it’s crucial to ensure that we have the necessary infrastructure in place to support its growth and maximize its potential. Thank you for shedding light on this important topic.

  4. animation course in kolkata
    animation course in kolkata
    September 15, 2023 at 11:39

    This is a Nice article, you highlight the AI, A solid artificial intelligence system isn’t complete without an efficient central processing unit (CPU) and graphics processing unit (GPU). Both are elements that enable processing in AI. A CPU can accomplish various tasks in a short time such as input, storing data, and transferring it out.

  5. Egor
    February 21, 2024 at 20:46

    Thank you for the article!!!

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