The Infrastructure Behind AI at Scale: What You Need to Know

With enterprises racing to build and deploy AI models at an unprecedented scale, the demand for high-performance infrastructure has never been greater. To give you an idea, the global AI market is expected to reach $1.3 trillion within the next decade, up from just $40 billion in 2022, according to a Bloomberg Intelligence (BI) report.  From NVIDIA’s AI supercomputers to Meta’s Research SuperCluster (RSC), even the most advanced AI systems rely on powerful infrastructure to train and fine-tune models efficiently. As AI adoption scales in the coming decade, growing businesses must invest in robust infrastructure and solutions to lead in this competitive market.

How Top AI Companies Scale AI

Top AI companies scale their operations using advanced hardware, efficient data systems and flexible solutions to train large models and serve millions of users daily. For example, Meta has invested heavily in custom hardware and data centres to support its AI operations. It uses the Research SuperCluster (RSC), one of the world’s fastest AI supercomputers for training large language models and other AI applications. The RSC leverages NVIDIA GPUs and high-speed networking like InfiniBand to handle petabyte-scale datasets, showing how the right infrastructure can support efficient model training and fine-tuning. Similarly, OpenAI used Microsoft’s AI Supercomputer built with high-end GPUs and networking solutions like InfiniBand for their 175-billion-parameter GPT-3 model, designed to support a wide range of tasks across different domains. 

Just like leading companies, growing businesses also face several challenges in scaling their AI workloads, from securing the right compute resources to optimising infrastructure for efficient performance. Let’s see what challenges companies face while building AI at scale.

Challenges Businesses Face to Scale AI

Scaling AI involves deploying complex models across vast datasets or user bases, a process that brings significant challenges for businesses such as:

• Compute Power: Scaling AI requires significant computational resources which can increase operational costs and complexity.
• Networking: Large-scale AI needs fast networks to connect GPUs and storage. Slow networks can delay data transfer, impacting training and inference
• Data Storage: AI relies on large datasets but traditional storage often can’t handle the speed and scale needed to manage large datasets.
• Scalability: AI Systems must scale dynamically with demand, both horizontally and vertically without performance drops
• Data Sovereignty: Compliance with regulations like GDPR requires data to be stored in specific regions which adds to the complexity of scaling AI in global operations.

Why Infrastructure Matters for AI at Scale

Scaling AI demands a robust infrastructure to tackle its challenges. Below we mention the essential components required to build AI at scale:

High-End Hardware

We offer high-end scalable GPU clusters like the NVIDIA HGX H100, NVIDIA HGX H200 and the upcoming NVIDIA Blackwell GB200 NVL72/36 with reference architecture developed in partnership with NVIDIA. The Hopper NVIDIA H100 and H200 offer 141GB of HBM3 memory and 3.3TB/s bandwidth, while the NVIDIA Blackwell GB200 NVL72/36 delivers exascale performance for AI at scale. These GPUs accelerate matrix operations critical for large-scale models. We also offer liquid cooling and certified high-performance storage, which ensures optimal thermal management and rapid data access to support demanding AI workloads.

Advanced Networking

High-speed networking is essential for seamless communication between GPUs and storage in distributed AI systems. We offer advanced networking solutions like NVLink and NVIDIA Quantum-2 InfiniBand with up to 900GB/s bandwidth for low-latency interconnects, minimising delays during multi-node training where thousands of GPUs must synchronise. This is ideal for large-scale training and inference tasks and maintains performance as the system scales to meet organisational needs.

Scalable Storage

AI workloads require storage that scales to petabyte levels with low latency. Our NVIDIA-certified WEKA storage with GPUDirect Storage support delivers high-performance, storage solutions for AI at scale. This ensures quick data delivery to GPUs, supporting intensive workloads like model training and scales seamlessly with growing datasets, maintaining operational efficiency.

On-Demand Access

If you have short-term or burst workloads, you can benefit from on-demand GPU access, avoiding long-term capital investments. We integrate our cloud GPU platform Hyperstack for on-demand GPU access, allowing organisations to scale temporarily for tasks like model retraining or product launches. This eliminates the need for permanent hardware commitments, providing a cost-efficient way to handle peak loads while maintaining scalability.

Data Sovereignty Compliance

Global operations require infrastructure compliant with regional data laws such as GDPR. We ensure data sovereignty with deployments in Europe and Canada, while all NexGen Cloud-owned infrastructure is hosted in data centres powered by 100% renewable energy. This means your AI operations meet stringent regulations like GDPR while being sustainable.

Additional Support and Management

Beyond these components, scaling AI requires ongoing support and management. We provide specialised support through Dedicated Technical Account Managers and MLOps engineers, offering end-to-end assistance from migration to optimisation. Our MLOps-as-a-Service covers the machine learning lifecycle—data preparation, training, deployment, and monitoring—while comprehensive management handles updates and security, reducing operational overhead and enhancing reliability.

Conclusion

Scaling AI requires more than raw compute power, it demands an integrated infrastructure that supports seamless model training, deployment and optimisation. With our AI Supercloud, you get access to high-end GPUs, advanced networking and scalable storage, all designed to meet the growing demands of AI workloads. We understand that each client has unique requirements, hence we offer custom hardware and software configurations, perfectly aligning with your operational needs and business objectives. 

Explore Related Resources

• How to Scale AI Training Like Meta: A Case Study
• How Scalable Infrastructure Supports Enterprise Growth
• How Enterprises Can Scale AI with GPU Clusters

FAQs

Why is high-performance infrastructure essential for scaling AI?

AI models require massive computational power, fast networking, and scalable storage to handle large datasets efficiently.

How does networking impact AI scalability?

High-speed networking solutions like NVLink and InfiniBand reduce latency and improve data transfer between GPUs and storage.

Why is scalable storage critical for AI workloads?

AI models rely on vast datasets, requiring high-speed, low-latency storage to prevent bottlenecks during training and inference.

What are the biggest challenges in scaling AI infrastructure?

The key challenges in scaling AI include securing sufficient compute power, managing network performance and ensuring efficient data storage.

How can businesses optimise infrastructure costs for AI scaling?

Using efficient resource allocation, dynamic scaling, and high-performance hardware helps reduce operational costs.

What role does compliance play in AI infrastructure?

The AI infrastructure must adhere to regional data laws, ensuring secure data storage and processing across different locations.