Artificial intelligence has already changed almost every industry and has become a key part of many business activities. Operating AI tools at such a scale requires an increasingly complex infrastructure. This need has led to the development of a modular AI infrastructure that scales up when needed.
In this article, we’ll go over the benefits of such hardware ecosystems, their use, and their long-term prospects as AI continues to evolve rapidly. Fast innovations are already the main feature of this approach to AI. As businesses adopt AI for their day-to-day tasks, they are also planning to adapt to new systems as they develop.
What Modular AI Infrastructure Means
Simply put, modular AI infrastructure refers to the ability to break down computer components into interchangeable parts. Modularity refers to both the hardware and software components of AI ecosystems. Hardware components that can be replaced include CPUs, GPUs, and specialized accelerators.
Software components include platforms like Kubernetes. These allow users to deploy and manage AI-related tasks across different platforms.
With a traditional, monolithic setup, if a user wants to upgrade their hardware ecosystem, they need to do so wholesale. It’s an expensive and technologically challenging proposition, which is why businesses prefer a modular system. With a modular system, an enterprise can change components one by one when needed and as new technologies arise.
How is AI used?
AI has quickly found its way into many industries, including businesses. For instance, reviews of 888Starz show that AI is widely used in the gambling industry. It’s used to provide customer service, randomize outcomes, and offer players game suggestions.
In recent years, AI has been added to the services of the HR industry, banking and finance, and almost every customer-facing service to cut costs. This means that there’s a growing need for scalable hardware, somewhat achieved through the use of modular infrastructure.
Drivers behind the Shift to Modularity
According to experts such as those at CryptoManiaks, there are a few reasons companies are moving towards modularity in their AI setups.
Scaling
The rise of AI requires a huge amount of computing power, and as more people use large language models, they become increasingly complex. Therefore, companies need to be able to scale up their performance regularly. Modular design allows them to swap GPUs and memory as soon as new hardware becomes available.
Energy Efficiency
Sustainability is one of the most important concerns for a modern high-tech company. Reducing carbon emissions is both a regulatory necessity and the best way to mitigate the environmental harm a company causes. Modular AI setups make the process easier by allowing users to power down components they are not using without jeopardizing operations.
Edge Computing Power
Edge computing growth refers to moving away from large systems and focusing instead on hardware that actually performs services and generates data. For instance, this means AI won’t be used as much to organize centralized data centers, but rather in factories, retail businesses, or vehicles. Modular infrastructure aligns perfectly with this goal because it enables businesses to build small AI systems dedicated to specific tasks.
Vendor Neutrality
Many businesses are worried about being tied to a single proprietary ecosystem. Modular infrastructure is based on open standards. That way, different parts can be sourced from different vendors. Mixing and matching components makes it easier to build a system that evolves and adapts as technology improves.
The Building Blocks
There are a few components that every AI hardware system needs and that should be part of any flexible, scalable setup.
The Computer Layer
The computer layer is the very foundation of the system. It includes CPUs, GPUs, and AI accelerators, such as TPUs and custom chips. These parts are known as composable infrastructure, meaning they are not tied to a single server. Instead, the components are dynamically pooled and allocated to specific workloads based on demand.
Storage Layer
AI workloads require a large amount of data, which must be stored securely and easily accessible. It’s also essential for the company to scale its storage capabilities easily. Modular SSD and NVMe storage arrays can expand as data grows. It’s also common to use file systems like Luster and Ceph to make data easier to access. That way, the performance doesn’t change as new data is added.
Cooling and Power Management
Power management and cooling are essential for AI systems. Modular cooling options, including liquid and immersion systems, allow users to increase their power output without producing excessive heat and thus jeopardizing their devices. Modularity in power distribution is also essential, especially when it comes to gradually expanding the systems over time.
Modular hardware infrastructure is key to expanding and scaling AI use. It allows users to adopt and add new tech, thereby remaining competitive quickly.
