Optimising AI Workloads: How Bespoke Hosting Maximises Performance
AI is predicted to be integrated into almost all aspects of our lives over the coming years, from our homes to the workplace, and entertainment to healthcare. While it has its controversies, it is clear AI is here to stay, with far-reaching potential across industries. But in order for AI technology to meet this potential, the correct foundations must be in place.
When we talk about hosting for AI, it is often described as a singular service or product, but in reality it is a broader category. AI workloads can range from training LLMs, to image recognition, to data processing - each with specific infrastructure requirements. Just as there is huge diversity in the application of AI, there needs to be diversity in the hosting of AI workloads - a one-size-fits-all solution does not meet the majority of use cases. In this insight, we will discuss how bespoke infrastructure allows you to run your AI workloads in the most effective way, and outline the benefits of this optimisation of your platform.
The unique demands of hosting for AI workloads
McKinsey reported that 65% of respondents stated that their organisations are regularly using gen AI, and that organisations are already seeing material benefits from incorporating AI (McKinsey Global Survey). The use cases for AI in businesses are widely varied - in some instances, AI models may be a business’s primary output, while in others, AI may only be used internally to streamline processes. The infrastructure requirements for these varied use cases differ. There are several elements to consider when it comes to hosting for your AI workloads.
AI computing is resource-intensive, and requires powerful servers to provide the significant power and performance needed. Additionally, these servers will need to be hosted in data centres that are equipped to meet the increased power and cooling requirements.
To meet these needs, a high-performance computing (HPC) solution, which provides powerful performance for high-compute workloads, is often used. Industry analysts have also observed an increase in private cloud infrastructure, and a shift away from public cloud, due to the increase in AI use (CIO). This diversification in infrastructure requirements has also led many businesses to adopt a hybrid model, combining HPC, private cloud, public cloud, and on-premise environments, assigning workloads to the environment best suited to handling them.
The majority of AI models use GPU servers. Graphic processing units (GPUs) are specifically designed to handle intensive graphics rendering tasks, and analyse large amounts of data faster than computers reliant on central processing units (CPUs). However, CPU-based servers, when configured correctly, can also be effectively utilised for AI development, testing and training.
The issue with off-the-shelf hosting for AI
Off-the-shelf cloud hosting can present challenges for businesses due to their generic nature, as opposed to a consultative approach that delivers bespoke solutions that work more effectively. This is exacerbated when it comes to hosting for AI.
Off-the-shelf solutions generally offer either a fixed amount of resources, or ‘levels’ of resources, with specific configurations, such as GPU-only models. While this may suit certain projects, such as short-term application testing, or lower-resource platforms, for many projects this can lead to significant inefficiencies.
Without the option for customisation, you may end up with either significantly more resources than you need, costing you unnecessary budget, or insufficient resources, causing performance issues for your project.
For more complex projects where you may need a mixed or hybrid infrastructure, this may either not be possible, or come at a significantly higher cost than you initially expected.
You may also run into issues when it comes to scaling your platform. As the technology continues to be developed, and integration into workplaces increases, it is likely that your AI hosting needs will grow. With standard solutions that have a low level of flexibility, scaling your resources can be slow, and come with potentially hidden costs.
The benefits of bespoke solutions for AI workloads
There are a significant number of benefits to using a bespoke solution for your AI workloads.
Bespoke infrastructure can be entirely customised, meaning you can choose the environments and components that best suit your workloads. Rather than fitting your workloads to a pre-configured environment, you fit the environment to your workloads. This optimisation means you can maximise the performance and benefits of your AI workloads, while saving money and reducing your power consumption by removing spending on unnecessary resources.
This customisation also allows you to allocate resources exactly where they are needed. Every component, whether GPU, CPU, or storage, matches the requirements of your workloads, meaning no over-provisioning and waste.
Unlike off-the-shelf solutions, bespoke AI solutions can be designed to seamlessly integrate with your existing IT infrastructure. Particularly when you use a hybrid cloud model, this reduces the complexity of your systems, providing a smoother user experience.
In conclusion
The development of AI technology is an evolving field, with new innovations requiring dynamic, flexible infrastructure. The set configurations offered by off-the-shelf solutions may not be flexible enough to meet all unique and developing requirements, and there is a clear need for affordable, bespoke hosting for AI workloads.
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Usman Ikhlaq
Usman Ikhlaq
Usman joined techUK in January 2024 as Programme Manager for Artificial Intelligence.
He leads techUK’s AI Adoption programme, supporting members of all sizes and sectors in adopting AI at scale. His work involves identifying barriers to adoption, exploring solutions, and helping to unlock AI’s transformative potential, particularly its benefits for people, the economy, society, and the planet. He is also committed to advancing the UK’s AI sector and ensuring the UK remains a global leader in AI by working closely with techUK members, the UK Government, regulators, and devolved and local authorities.
Since joining techUK, Usman has delivered a regular drumbeat of activity to engage members and advance techUK's AI programme. This has included two campaign weeks, the creation of the AI Adoption Hub (now the AI Hub), the AI Leader's Event Series, the Putting AI into Action webinar series and the Industrial AI sprint campaign.
Before joining techUK, Usman worked as a policy, regulatory and government/public affairs professional in the advertising sector. He has also worked in sales, marketing, and FinTech.
Usman holds an MSc from the London School of Economics and Political Science (LSE), a GDL and LLB from BPP Law School, and a BA from Queen Mary University of London.
When he isn’t working, Usman enjoys spending time with his family and friends. He also has a keen interest in running, reading and travelling.
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