AI infrastructure was once viewed as a supporting tool for business operations. Today, it has become a core driver of digital transformation, enabling companies to process vast amounts of data, enhance AI-driven automation, and scale operations effortlessly.
With the right AI infrastructure, businesses can optimize predictive analytics for better decision-making, accelerate AI inference to reduce latency and improve performance, and enhance operational efficiency by automating complex tasks.
But, what exactly is AI infrastructure and how does it compare to traditional IT systems? Let’s dive in.
What is AI Infrastructure?
AI infrastructure is generally a combination of hardware and software designed to support the development and adoption of AI at scale. It includes high-performance servers, GPUs, fast data storage, and high-quality networks to handle heavy AI workloads and accelerate Machine Learning models.
SuperMicro emphasizes that AI infrastructure must have a flexible and scalable architecture in order to handle increasing computing demands. Modern AI servers should support GPU or TPU-based accelerators, which are designed to process billions of operations per second, allowing AI models to learn and adapt faster.
AI Infrastructure vs Traditional IT Infrastructure
AI infrastructure may still feel unfamiliar to some companies. This is because the majority of companies so far still use traditional IT infrastructure. Basically, both have fundamental differences in terms of architecture, performance, and its purpose in supporting computing workloads. In detail, here are the differences between the two.
| Differences | Traditional IT Infrastructure | AI Infrastructure |
| Architecture | A standard CPU server for handling common computing such as database management, business applications, and the cloud. | GPUs, Tensor Processing Units (TPU), and AI accelerators to speed up data processing and ML. |
| Performance | Limited because it relies on general hardware. | Faster and more efficient thanks to NVMe SSDs and low-latency networking to handle large data volumes. |
| Scalability | Need extra cost and time for capacity scale up. | Integration with cloud and edge computing without the need for costly hardware investments. |
| Flexibility | Limited to on-premises data centers. | Supports container-based architecture and orchestration such as Kubernetes to manage workloads more easily. |
| Workload Optimization | Relies on sequential processing, making it difficult to process big data. | Can optimally handle deep learning, machine learning, and predictive analytics workloads. |
How Important is AI Infrastructure for Businesses?
Companies’ reliance on AI to increase efficiency, drive innovation, and improve competitiveness makes AI infrastructure a key component in ensuring business smoothness, efficiency, and scalability. Large-scale data processing capabilities help modern businesses make accurate decisions based on data.
This technology also enables automation of business processes, from data analysis, customer support, to fraud detection to be more productive. As the business grows, AI infrastructure can dynamically optimize the use of resources without making large investments in hardware. That way, businesses can be more efficient in optimizing operational costs without the need to spend large capex.
Advantages of Using AI Infrastructure
Based on the above explanation of the difference between AI infrastructure and traditional IT infrastructure, this technology has many advantages that are not obtained from legacy solutions. Here are the four main benefits that companies get when using infrastructure AI.
Improve Scalability and Flexibility
While traditional IT infrastructure requires procurement and configuration of additional hardware when increasing capacity, this is not the case with infrastructure AI. It offers greater flexibility with cloud and edge computing support. The container-based and orchestration architecture allows companies to easily customize computing capacity as needed.
High Performance and Speed
Infrastructure AI is powered by GPUs, TPUs, and other AI accelerators for faster parallel processing. AI models can also be trained to be more efficient and reduce time during AI algorithm development and deployment. High-speed storage technologies and low-latency networks can accelerate data transfer for real-time analytics.
Better Security and Compliance
Equipped with tighter security layers, including data encryption, Role-Based Access Control (RBAC), and real-time AI-based anomaly detection systems. AI infrastructure also helps companies comply with industry regulations by providing audit trails according to GDPR, HIPAA, or ISO 27001 standards.
Cost Efficiency
Higher automation makes infrastructure AI less dependent on manual human resources for data management and analytics. Optimization of computing power and storage can help companies reduce energy consumption and investment in expensive physical hardware, which is associated with operational cost savings.
Read More: What is Network Switch and Why is it Important?
What is Needed to Build the Foundation of AI Infrastructure?
Building an optimized AI infrastructure requires a combination of hardware and software that can handle AI workloads with high efficiency. According to SuperMicro, there are two important components needed: hardware and software.
Hardware: Including AI Servers, Data Storage, Networking, and Compute Performance
There are a number of hardware needed for infrastructure AI implementation. These range from High-Performance Computing (HPC) with specialized processors and high computing power (including GPUs, TPUs, Field Programmable Gate Arrays, and AI-Optimized CPUs), high-speed storage (including NVMe SSDs, PFS, and object storage), and high-speed networks that have low latency and high bandwidth. Some of the networking technologies used in infrastructure AI include InfiniBand with ultra-low latency and high bandwidth, 100GbE or 200GbE ethernet for fast transfer of large amounts of data, and Remote Direct Memory Access (RDMA) to improve efficiency in AI clusters.
Software: Includes AI Integration with Cloud/Hybrid Cloud
Besides hardware, this technology also requires software to manage, optimize, and orchestrate resources. Some of these software are AI frameworks and libraries (including TensorFlow, PyTorch, APIs for deep learning, and MXNet to optimize speed), resource orchestration and management (including AI containers Kubernetes and Docker, Apache Mesos and SLURM, and Ray for distributed processing), and MLOps or Machine Learning Operations Tools (MLflow, Kuberflow, and Weights & Bases).
How to Optimize Business to Support AI?
A strategic approach is needed to optimize business support for AI, from investing in advanced hardware, implementing cooling technology, and implementing AI-based infrastructure management solutions. First, make sure to invest in high-performance hardware that can handle AI-intensive workloads. The use of the right hardware can ensure the ability to efficiently process large volumes of data.
The power requirements of modern AI chips make heat management a major challenge. This requires cooling technologies that can improve operational efficiency and are more energy-efficient to optimize hardware performance. Managing complex AI requires the right management tools to prevent and solve problems across the infrastructure stack. This solution can help companies reduce downtime, improve operational efficiency, and ensure AI infrastructure is in optimal condition.
AI Infrastructure Recommendations for You from MBT
Mega Buana Teknologi (MBT) recommends NetApp and SuperMicro as AI infrastructure solutions to meet your business needs.
NetApp
NetApp offers solutions that can help companies build a strong foundation to optimize the performance and productivity of AI initiatives, with secure and efficient data management. A unified approach enables NetApp to help companies break down data silos and simplify management, including efficient integration and seamless data mobility.
The ability to process data five times faster through the pipeline makes NetApp the right solution to support data-driven decision making. Dataset copy processes that can be completed in seconds can improve operational efficiency and reduce downtime. AI infrastructure configuration is faster and easier in just 20 minutes using Ansible.
Here are three advantages of NetApp infrastructure AI solutions:
- Seamless data mobility thanks to AI-optimized data management. This allows businesses to instantly classify, move, and present data across hybrid multi-cloud environments.
- Maximum performance, efficiency, and high scalability thanks to compatibility with optimized AI GPUs for every workload across the AI lifecycle.
- Real-time protection against cyber threats and anomaly detection thanks to the Autonomous Ransomware Protection feature that can track potential threats to the data that will be run for your AI project needs.
SuperMicro
SuperMicro’s AI infrastructure is designed to meet compute-intensive needs in AI and deep learning applications. SuperMicro provides a variety of systems for various AI workloads, including real-time inference. Here are three advantages of SuperMicro:
- Modular design for flexible system configuration according to users’ specific needs. Easier infrastructure customization and scalability as business AI needs evolve.
- To address the thermal challenges of high AI workloads, SuperMicro offers efficient cooling solutions to ensure optimal performance, while reducing energy consumption and operational costs.
- SuperMicro can be integrated with the latest technologies, including NVIDIA GPUs that offer high performance and compatibility with modern AI applications.
- SuperMicro is an infrastructure AI solution that can provide up to 256 GPUs in one scalable unit, so it can handle increased computing density. The implementation of liquid cooling can reduce energy consumption by up to 40 percent. SuperMicro can reduce implementation complexity thanks to end-to-end design, integration, and testing.
Why NetApp and SuperMicro Are the Right Choice for You?
NetApp and SuperMicro are ideal solutions to support AI infrastructure in your business. Both are designed to handle a wide range of AI computing needs, including large amounts of data storage, efficient processing, and high scalability to ensure optimal performance in AI implementations.
NetApp excels in supporting software- and hardware-based, managed AI data management, while SuperMicro powers a scalable, customizable AI/ML computing infrastructure.
NetApp and SuperMicro offer the best solution to support businesses in building a flexible and scalable AI infrastructure according to company needs. NetApp acts as a bridge between AI in the cloud and on-premises, while SuperMicro enables on-premises integration with SDS that has been optimized for cloud environments.
Get the Best AI Infrastructure Solution on MBT
Mega Buana Technology as part of CTI Group offers the best AI infrastructure services to help you adopt the sophistication of NetApp and SuperMicro technology smoothly. Supported by a team of professionals, MBT can optimize operations, save costs, and increase your business productivity.
Contact us now for the best AI infrastructure solutions to suit your unique business needs.
Author: Ervina Anggraini – Content Writer CTI Group
