AI Workloads Need Purpose-Built GPU Cloud Infrastructure: Here's Why 

The rapid demand for artificial intelligence (AI) and machine learning (ML) is projected to contribute $15.7 trillion to the global economy by 2030, with 92% of companies set to invest in AI over the next three years.  

AI’s resource-intensive nature, significant power draw, and reliance on specialized hardware like GPUs are pushing the limits of traditional infrastructure. The high cost of capital,  compute and energy requirements are exposing a serious shortfall: legacy clouds and general-purpose data centers were never built for the scale or intensity of modern AI workloads. 

The Problem with General-Purpose Cloud Platforms 

Legacy cloud infrastructure was built for web apps, SaaS, and distributed storage - not multi-GPU training, high-throughput inference, or massive model checkpoints. The result? Shared GPUs, limited networking, and generic cooling that lead to job slowdowns, instability, and inference performance that degrades rapidly at scale. 

For many companies, making large capital investments in dedicated AI infrastructure - especially GPU clusters, advanced networking, and cooling - is not economically viable. These resources are often only needed during peak periods or specific deployment windows, leaving expensive infrastructure underutilized outside those times.  

To get access to the scalability and cost-effective infrastructure required, companies are looking to specialized GPU cloud providers such as IREN Cloud™. These services are purpose-built for AI - and can scale as needs evolve. 

AI Compute Demands a New Class of Infrastructure 

AI workloads aren’t just larger - they’re fundamentally different. They require parallel compute, tightly synchronized GPUs, and extremely high throughput across network and storage layers. Power draw per rack can exceed 100 kW, pushing well past the limits of traditional cooling systems and electrical design. 
 
Traditional infrastructure lacks the hardware and power delivery needed to support dense AI clusters. GPU cloud infrastructure must deliver low-latency node-to-node networking and consistent power to keep up. And as rack density rises, direct-to-chip liquid cooling - such as IREN’s currently under construction Horizon 1 facility - becomes essential to maintain performance without thermal throttling. 

The Rise of GPU Cloud Infrastructure Purpose-Built for AI Teams and Developers 

Faced with these challenges, companies are choosing GPU cloud infrastructures that are specifically built for AI workloads. These environments provide the computing power and specialized GPUs needed for AI training and inference as well as the speed, flexibility and scale required for production workloads.  

The ability to seamlessly transition between training and inference is crucial. Specialized GPU cloud solutions provide the infrastructure required for both AI training and inference needs. Providers like IREN deliver the infrastructure  AI workloads demand, with established power, land and power-dense data center capacity already in place.  

When evaluating a GPU cloud solution, it’s important to look for: 

• Purpose-built AI infrastructure: designed specifically for AI workloads 
• Scalable GPU clusters: flexible scaling to meet both training and inference needs 
• Next-generation ​​accelerated computing resources​​​​​​: compatible with NVIDIA H​​opper​​​​ and NVIDIA Blackwell architectures 
• Advanced cooling systems: engineered for power-dense AI workloads 
• Sustainable, grid-connected power infrastructure: grid-connected energy-efficient data centers utilizing 100% renewable power 

Infrastructure Needs for Large Language Models (LLMs) 

AI model acceleration is driving specific GPU cloud infrastructure requirements. Models like Meta’s Llama 4 Herd are not only GPU compute-intensive but need environments that can scale with the latest LLM release schedules and specifications. 

By choosing a GPU cloud solution such as IREN Cloud™, you can scale your AI training and inference needs to align with model demands easily. IREN Cloud™ also includes NVIDIA H​opper​​ GPUs, NVIDIA Quantum-2 400Gb/s InfiniBand networking and multi-node clusters required to efficiently run ​open source models like ​Llama 4 for AI training and inference. 

Why Speed, Transparency and Support Matter 

Many GPU cloud solutions offer infrastructures for AI workloads, but operational execution can fall short.  Delays in deployment, lack of pricing transparency, and limited on-site support create friction when you need to move fast. 

Common pitfalls to watch for are: 

• Provisioning speed: does the provider have established facilities, with power, land and data center capacity ready to go? 
• Engineering access: will you have direct access to engineers experienced with AI infrastructure who can provide tailored guidance? 
• On-site support: is there 24/7 experienced support and services you can leverage when issues arise? Is the provider using 3^rd party data center facilities and 3^rd party support staff? 
• Pricing transparency: are all charges transparent, or could you face unexpected or hidden costs? 
• Data transfer fees: are there ingress and egress fees, are they transparent and how are they structured? 

IREN Cloud™ Built for AI 

By choosing infrastructure built for AI, you can avoid these pitfalls and future-proof your business for evolving needs. 

IREN Cloud™ has been designed from the ground up for AI and inference workloads and their dense-compute needs. As IREN owns and operates the data centers and latest-generation GPUs, you get a highly-performant AI cloud service, service transparency, cost-effective predictable pricing, and rapid scalability. Experienced AI engineers, on-site services, and support utilizing our own staff are also built in. 

With no data ingress or egress fees, IREN is well-placed to be your partner of choice for high-performance AI and ML workloads. 

Ready to Scale Your AI Infrastructure? Contact us for an AI cloud discussion