October 1, 2024
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You’re an entrepreneur, running a mid-sized business that manufactures eco-friendly packaging solutions. Your team just completed a trial run with a machine learning model to optimise your supply chain. The results were promising as the costs dropped by 20 and reduced waste significantly. Now, you want to scale these results across the business with AI. Just like leading companies like Amazon, Tesla and Unilever have been using large-scale AI to improve their logistics, predictive maintenance and customer operations. Another example could be BMW adopting AI at scale to improve vehicle production. An AI-powered smart maintenance system monitors conveyor operations during assembly. By analysing data from existing components, the system proactively detects potential issues, preventing unexpected stoppages and ensuring a seamless production flow. As a result, BMW has been able to save around 500 minutes of assembly line downtime annually.
According to a report by PwC, AI could contribute up to $15.7 trillion to the global economy by 2030. However, scaling AI is not just about running larger models. It’s also about training massive datasets, fine-tuning algorithms and deploying solutions effectively. In our latest article, we explore how businesses can scale AI in 2025.
The Challenges of Scaling AI for Business Impact
Large-scale AI training requires massive computational power. If we consider the latest innovations of OpenAI and Meta, their generative AI models rely on hundreds of billions of parameters. And the fact that training such large AI models with billions of parameters can emit over 626,000 pounds of carbon dioxide equivalent, nearly five times the lifetime emissions of an average American car including its manufacturing (University of Massachusetts Amherst Report). Your existing infrastructure may not be optimised for the level of performance required for large AI workloads. Scaling infrastructure to meet these demands without exacerbating energy inefficiencies is a major challenge. More efficient and purpose-built hardware like NVIDIA H100 GPUs and high-performance storage systems may ensure that power and computational resources are used efficiently.
Want to learn more? Check out our article on The Hidden Environmental Impact of Large-Scale AI
But that’s not all, many businesses face significant infrastructure challenges while attempting to scale their AI systems. Event AI leaders like Meta faced major challenges in building their Generative AI infrastructure. To support models like Llama 3, Meta developed two data center-scale clusters, each consisting of 24,576 NVIDIA H100 Tensor Core GPUs. These clusters were interconnected using advanced networking technologies, including Remote Direct Memory Access (RDMA) over Converged Ethernet (RoCE) and NVIDIA Quantum-2 InfiniBand, each providing 400 Gbps endpoints. This optimised setup helped Meta handle hundreds of trillions of AI model executions per day, facilitating the training of increasingly complex models. Learn how to scale AI training like Meta in our latest article.
Similar to Meta, scaling businesses often face challenges due to inefficient infrastructure. Many existing systems are not equipped to manage the massive data volumes and intricate computations demanded by advanced AI models. This leads to slow processing time, inadequate data storage capacity and difficulty integrating with legacy systems. For training LLMs at scale to build LLM-based chatbots or code generation systems, powerful hardware like NVIDIA Hopper GPUs with interconnect technologies like NVIDIA Quantum-2 InfiniBand can reduce latency and provide faster data transfer.
Now, let’s return to your eco-packaging company. Scaling AI will involve integrating diverse datasets like supplier information, consumer feedback and market trends. You will need robust computing resources, scalable storage and efficient data management. Without this, the AI system that promised so much during its trial phase might struggle to deliver consistent results. But the question arises: Where do you find the infrastructure and expertise to support this?
Why Businesses Need Robust Infrastructure to Scale AI
According to PwC’s 2024 Global Investor Survey, 73% of investors believe companies should implement AI solutions at scale. While 66% expect the companies they invest in to deliver productivity increases from AI to boost profitability. This shows the importance of businesses adopting AI at a scale not only to attract investment opportunities but also to improve their overall operations. However, achieving this is far from simple, it requires robust and powerful infrastructure to support the demands of advanced AI applications. This is exactly why scaling businesses are partnering with cloud providers to access optimised scalable AI solutions and managed services like MLOps to streamline AI lifecycle management.
How the AI Supercloud Enables Large-Scale AI Deployment
At the AI Supercloud, we understand the intricacies of large-scale AI deployment and we bring advanced technology and expertise to meet your needs.
Optimised Hardware
We provide powerful and optimised GPUs for scaling AI including the NVIDIA HGX H100 NVIDIA HGX H200 and the latest NVIDIA Blackwell GB200 NVL72 Blackwell GPUs with advanced liquid cooling, low-latency networking, high-performance WEKA storage and NVIDIA Quantum-2 InfiniBand to offer peak performance at scale.
Fast Delivery and Deployment
With our streamlined processes for rapid delivery and deployment of GPU clusters for AI. We make it possible to scale up your AI infrastructure to thousands of GPUs in as little as 8 weeks, ensuring you can scale quickly and meet your business demands without delays.
For workload bursting, you can also scale up and down with Hyperstack, gaining immediate access to extra GPU resources as your workload expands. Our on-demand integration allows you to adapt to growing AI demands while maintaining peak performance at all times.
Highly Scalable Storage Options
Our high-performance WEKA Data Platform offers highly scalable storage solutions for businesses that need to manage massive datasets. Our platform supports the entire data lifecycle with top-tier performance, ensuring your AI models are built on fast and reliable access to critical data. This robust data management solution makes it easier to handle AI workloads at scale with no compromise on storage capabilities.
MLOps
Scaling AI goes beyond hardware, it’s about ensuring smooth operations from training through deployment. The AI Supercloud offers expert-managed MLOps services, guiding you through the entire machine learning lifecycle. With our advanced automation tools and support for end-to-end AI lifecycle management, we streamline MLOps processes and boost productivity and scalability for your business.
Your AI Journey Starts with Us
Scaling AI requires a robust cloud infrastructure that supports your business needs. With cutting-edge hardware, personalised solutions and expert support, the AI Supercloud helps businesses scale their AI operations with peak efficiency.
Book a call with our experts to identify the ideal AI solutions that align with your budget, timeline and technologies.
FAQs
What is the benefit of scaling AI in my business?
Scaling AI can significantly enhance operational efficiency, improve decision-making, and reduce costs. It enables your business to get insights from large datasets and optimise processes for greater profitability.
How long does it take to deploy AI infrastructure at scale?
With AI Supercloud's streamlined process, you can get GPU clusters in as little as 8 weeks for quick setup and fast scaling to meet business demands.
What hardware do I need to scale my AI?
To scale AI effectively, you’ll need powerful and optimised hardware such as NVIDIA HGX H100 or NVIDIA HGX H200 GPUs, which deliver the computational power required for training large AI models and processing massive datasets efficiently.
How does MLOps help in scaling AI?
MLOps streamlines the machine learning lifecycle, from deployment training, ensuring smooth, automated processes. It enables efficient model monitoring, updates, and scaling, which is vital to keeping your AI solutions optimised.
What types of data storage do I need for large-scale AI applications?
You will need highly scalable, high-performance storage like the WEKA Data Platform on the AI Supercloud to manage vast datasets efficiently. This ensures your AI models are built on reliable, fast data access and provides the storage needed for large-scale AI workflows.
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