5 Key Components of AI Infrastructure

A majority of businesses do acknowledge the importance of AI yet only a few manage to successfully implement it due to infrastructural challenges. To break through these barriers, organisations need to understand that building and deploying scalable AI solutions requires a robust infrastructure. This infrastructure, often referred to as the AI stack consists of several key components that work together to support the entire AI lifecycle. This could go from data ingestion and processing to large AI model training, deployment and monitoring. In this article, we'll explore the five essential components of an AI infrastructure.

What is AI Infrastructure?

An AI infrastructure refers to the foundational framework that supports the development, model deployment and management of artificial intelligence solutions. It consists of hardware, software and networking components. These components are necessary to process large volumes of data, train machine learning models and deploy AI applications. With this AI infrastructure, you can easily handle complex computational tasks, storage requirements and data flows essential for AI workflows

5 Key Components of AI Infrastructure 

Here are 5 key components of an AI Infrastructure: 

Powerful Resources

A well-built AI Infrastructure requires high-performance GPUs and CPUs. These resources enable you to handle intensive tasks like training large-scale AI models and running complex algorithms. So, you get faster processing and optimal performance for AI applications. At Hyperstack, we offer on-demand access to the latest NVIDIA GPUs like the NVIDIA H100 SXM and the NVIDIA H100 PCIe so you can run even the most intensive AI workloads efficiently.

Storage Solutions

AI infrastructure ensures you have robust storage solutions capable of managing vast data. This storage is essential for storing your large datasets used in AI model training and for handling model parameters, checkpoints and intermediate results. Hence, access to efficient storage solutions guarantees quick data retrieval to support your model's scalability and performance.

At Hyperstack, we provide comprehensive storage solutions designed for diverse workload needs. Our NVMe Block Storage, the default storage option offers up to three configurations depending on the VM. It delivers high-speed data transfer, local NVMe storage within GPU nodes for maximum performance, and persistent storage that retains data even during VM shutdowns. We also offer Shared Storage Volumes (SSVs), a network-based SSD storage solution with data replication across multiple servers for enhanced reliability and persistent storage accessible across multiple VMs, ensuring secure and scalable data management.

Optimised Networking

AI infrastructure provides optimised networking capabilities to facilitate efficient data transfer and communication within your AI systems. This includes low-latency networking for real-time applications and distributed environments. At Hyperstack, we offer high-speed networking of up to 350Gbps for several GPU options such as the NVIDIA A100 PCIe with NVLink, NVIDIA H100 PCIe and NVIDIA H100 PCIe with NVLink. Training models such as Llama 3.1-70B or other open-source models, which demand extensive datasets and significant computational power, become much more efficient with high-speed networking that offers high throughput and low latency.

AI-Ready Operating System

Developers often invest significant time configuring system drivers and dependencies to establish an environment suitable for AI development. This process can be complex and time-consuming, diverting focus from core development tasks. To streamline this setup, Hyperstack offers AI-ready virtual machine images with CUDA and Docker pre-installed. CUDA is essential for leveraging NVIDIA GPUs in AI workloads, while Docker facilitates consistent and isolated application environments. By providing these pre-configured images, Hyperstack enables developers to initiate AI projects without the delays associated with manual environment setup.

Security and Compliance

AI infrastructure ensures robust security measures to protect your data and AI models. This includes encryption, access controls and compliance with regulations. With secure infrastructure in place, you can confidently deploy and manage your AI applications while safeguarding sensitive information. At Hyperstack, we prioritise the security and reliability of our infrastructure to ensure your data is protected and accessible. Our data centres hold SOC 2 Type II certification, validating the implementation of robust controls for the security, availability, processing integrity, confidentiality and privacy of data. 

New to Hyperstack? Sign Up Now to Get Started with Hyperstack

FAQs

What is AI infrastructure?

AI infrastructure is the foundational framework supporting the development, deployment and management of AI solutions consisting of hardware, software and networking components.

Why are compute resources important in AI infrastructure?

Compute resources like high-performance GPUs are essential for training complex AI models and executing inference tasks efficiently.

How does AI infrastructure handle data management?

AI infrastructure implements robust data storage and management solutions to handle large volumes of data, ensuring integrity, quality and accessibility.

Why is scalability important in AI infrastructure?

Scalability ensures AI infrastructure can grow to meet increasing data volumes and complex model demands over time.