October 1, 2024
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Enterprises face complexities in managing AI workloads, from upgrading infrastructure to overcoming performance bottlenecks. However, the right AI strategy can help businesses achieve significant ROI by improving efficiency, decision-making and customer experiences. According to PwC, AI could add up to $15.7 trillion to the global economy by 2030. So, how can enterprises scale AI without the headache of complex infrastructure? The AI Supercloud offers the ideal solution for AI at scale. Keep reading to learn how the AI Supercloud can help your enterprise scale AI operations.
Why Scaling Enterprise AI for Business is Important
In 2025, enterprises can no longer afford to take risks, they must scale their AI operations to stay ahead of the competition and build market-ready solutions with AI. Here’s why scaling AI is essential for enterprises:
- Increased Complexity of AI Workloads: AI models have become more intricate and require more computational power, storage and faster data processing than ever. Enterprises must have robust and scalable infrastructure to handle these growing demands.
- Shift from Small-Scale to Enterprise-Wide AI: Enterprises are moving from small-scale AI experiments to broader implementations across departments to leverage AI’s full potential to drive innovation.
- Improved Efficiency: Scaling AI automates repetitive tasks, improves resource allocation and reduces human error to help teams focus on high-level strategies and decision-making.
- Better Decision-Making: With AI at scale, enterprises can make data-driven decisions faster and with greater accuracy. Real-time insights and predictive analytics lead to improved forecasting, risk management and informed decisions across all business levels.
- Cost Reduction: By scaling AI, businesses streamline operations, optimise supply chains and minimise inefficiencies. AI helps achieve economies of scale, making resource usage more cost-effective.
The Challenges of Scaling Enterprise AI
As AI adoption accelerates, enterprises face significant challenges in scaling their AI infrastructure. These challenges can hinder AI deployment and limit business value from managing hardware complexity to ensuring regulatory compliance.
Infrastructure Complexity
Scaling AI requires a robust infrastructure supporting high-performance computing, seamless software integration, and system reliability. However, managing this infrastructure is a major hurdle for enterprises as they need to balance performance with cost efficiency. Many businesses invest in AI hardware without optimising resource utilisation, leading to underused infrastructure and higher operational costs. A lack of expertise in configuring AI-ready environments can further slow down deployment and increase maintenance burdens. Without a robust and scalable infrastructure, enterprises delay AI innovation.
Performance Demands
AI models have become complex, requiring massive computational power. Just to give you an idea, training state-of-the-art LLMs like GPT-3 can consume around 1,300 megawatt-hours of electricity, as proven by reports.
To train such models, enterprises require high-end GPUs that offer superior performance while being energy efficient. This is why leading giants like NVIDIA are designing the most advanced chips and architectures to support AI's growing needs. For instance, NVIDIA’s latest Blackwell GB200 NVL72 delivers significant improvements in efficiency and processing power to handle scaling AI operations.
But here’s the catch, enterprises must carefully select and configure hardware to fully leverage these benefits. Without optimised hardware, businesses may struggle to keep pace with competitors who can deploy and train models faster.
Flexibility and Scalability
AI workloads are unpredictable. Enterprises often experience fluctuating demand with some projects requiring thousands of GPUs for short-term use, while others need consistent access to high-performance computing. But the catch here is that traditional IT infrastructure is often rigid, making it difficult to scale up or down.
Many enterprises either over-provision resources, leading to wasted resources or under-provisioning causing performance bottlenecks. To overcome this, businesses need solutions that allow for on-demand workload bursting and dynamic scaling without long-term infrastructure commitments.
This is why on-demand cloud platforms offer flexible resource allocation can help enterprises meet workload demands efficiently. With on-demand GPU access to powerful GPUs, businesses can scale without upfront hardware investments.
Data Sovereignty
Enterprises that handle sensitive data must follow strict regulatory requirements to avoid legal risks. Regulations like GDPR in Europe impose strict guidelines on data privacy, requiring organisations to process and store AI workloads in certified data centres. Non-compliance can result in hefty fines and reputational damage, which no enterprise would want to face.
Beyond regulatory concerns, sustainability has become a key factor in AI infrastructure decisions. AI models require immense computational power, leading to high energy consumption and increased operational costs. A report published by Morgan Stanley states that by 2030, AI is expected to drive data centre emissions to three times their current levels. Greenhouse gas emissions from these facilities could reach 2.5 billion tonnes annually. Hence, choosing sustainable and energy-efficient data centres can help enterprises lower costs and improve their AI operations.
The AI Supercloud for Scaling Enterprise AI
Ready to scale your enterprise AI operations? Partner with the AI Supercloud for a flexible, powerful and reliable infrastructure for AI workloads. Let’s see how the AI supercloud can support your enterprise AI:
Optimised Hardware
The AI Supercloud offers the latest NVIDIA GPUs, such as the upcoming NVIDIA Blackwell GB200 NVL72/36, NVIDIA HGX H100 and the NVIDIA HGX H200, to handle the most demanding AI workloads. We optimise our hardware with:
- Liquid Cooling Technology to maintain optimal performance in high-performance computing (HPC) environments.
- NVIDIA-certified WEKA storage for seamless data processing with low latency.
- NVIDIA Quantum-2 InfiniBand for quick data transfer and high-throughput environment.
We also offer reference architecture, developed in partnership with NVIDIA to ensure that enterprises can benefit from a proven framework that aligns with best practices in the industry. Whether enterprises are running complex models or processing massive datasets, the AI Supercloud ensures businesses can scale their AI without having to worry about low-performance hardware.
Learn more about Optimised GPUs for AI Scaling here!
Customised Solutions
Every enterprise has unique needs and workloads, which is why the AI Supercloud provides personalised solutions that ensure superior performance. Our platform offers flexible configurations such as GPU, CPU, RAM, storage and middleware. This allows enterprises to choose the most appropriate combination of resources for their specific use cases for performance and cost efficiency.
With customised solutions, enterprises can fine-tune their AI infrastructure to match the requirements of different applications, such as for running deep learning models, natural language processing (NLP) tasks or computer vision projects.
Managed Kubernetes and MLOps Support
Scaling AI operations requires more than just powerful hardware; it also demands robust orchestration and workflow management. The AI Supercloud delivers fully managed Kubernetes environments, which streamline the deployment and scaling of containerised applications across clusters of machines. Kubernetes has become the industry standard for managing AI workloads due to its flexibility and scalability, making it ideal for enterprises that need to manage large-scale distributed AI models.
The AI Supercloud goes further by integrating MLOps-as-a-Service, providing end-to-end support for the entire machine learning lifecycle. From model training to deployment and ongoing management, enterprises receive expert guidance and continuous support throughout their AI journey. This ensures that enterprises can build, deploy, and maintain high-performance AI models without needing to manage the underlying infrastructure themselves.
Burst Scalability with Hyperstack
Traditional infrastructure requires businesses to either over-provision resources to accommodate peak demand or risk under-provisioning during times of low demand. The AI Supercloud solves this problem through burst scalability through our on-demand cloud GPUaas platform Hyperstack which enables businesses to scale their workloads on demand.
With Hyperstack, enterprises can rapidly increase their computational resources when necessary, without the need for long-term commitments or infrastructure investments. This is invaluable for businesses with unpredictable workloads, such as those involved in research, experimentation or seasonal AI tasks. The ability to scale up on demand ensures that businesses only pay for the resources they need when they need them, offering significant cost savings.
Data Sovereignty and Sustainability
Our platform’s deployments in Europe and Canada ensure that businesses can maintain full data sovereignty, complying with local regulations such as GDPR in Europe and various privacy laws in North America. By processing and storing AI workloads within these jurisdictions, enterprises can mitigate the legal and security risks associated with cross-border data transfer.
All NexGen Cloud fully managed infrastructure is hosted in data centres powered by 100% renewable energy. So, enterprises are assured of sustainable solutions to accelerate and scale their AI operations.
Conclusion
Scaling AI is critical for enterprises aiming to stay competitive in 2025. With the right AI infrastructure, enterprises can overcome complexity, ensure flexibility and achieve remarkable ROI. Our AI Supercloud does exactly that, with customised solutions and robust infrastructure, enterprises can scale their AI workloads with less complexity. It's time to plan your AI scaling strategy for long-term growth and innovation. Talk to our Solutions Engineer today and discover the best solution for your project’s budget, timeline, and technologies.
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FAQs
Why is scaling AI important for enterprises in 2025?
Scaling AI helps enterprises stay competitive, streamline operations and improve decision-making.
What are the main challenges of scaling AI in enterprises?
The main challenges of scaling AI in the enterprise include infrastructure complexity, performance demands, flexibility in scaling resources and meeting data sovereignty requirements.
How can enterprises overcome infrastructure complexity when scaling AI?
With our AI Supercloud, enterprises can manage infrastructure complexity with our flexible configurations and optimised hardware.
How does the AI Supercloud support burst scalability for AI workloads?
The AI Supercloud offers burst scalability through the Hyperstack platform, enabling enterprises to scale resources on-demand without the need for long-term commitments.
Can the AI Supercloud support AI model training?
Yes, the AI Supercloud provides the necessary infrastructure for training large-scale AI models for enterprises with managed Kubernetes and MLOps support for streamlined model deployment and management.
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