From data-driven insights to streamlining operations and improving customer experiences, AI has become an integral part of modern enterprise strategies. However, the scale and complexity of these AI workloads require advanced infrastructure designed to meet their demanding requirements. For instance, large language models like OpenAI's GPT and Meta Llama 3 require the processing of billions of parameters during training, demanding exceptional computational power. Similarly, computer vision workloads such as developing algorithms for autonomous vehicles require analysing terabytes of data in real-time simulations and intensive model training processes. Such workloads require optimised hardware solutions that deliver superior compute power, efficient data handling and seamless networking for massive-scale AI deployments. In our latest article, we explore why you need optimised GPU solutions for large-scale AI workloads.
Large-scale AI workloads require more than just powerful hardware, they demand carefully tailored infrastructure optimised for specific needs. Let’s explore why you need optimised hardware for your large-scale AI deployments:
AI workloads are as varied as the companies that deploy them. From training expansive neural networks to running real-time inference tasks, each use case has unique computational demands. To meet these, the AI Supercloud offers the latest cutting-edge NVIDIA GPUs, including the NVIDIA HGX H100, NVIDIA HGX H200 and the much-anticipated NVIDIA Blackwell GB200 NVL72/36. These GPUs are designed to support extreme parallelism, precision and scalability. Whether you’re working on large language models with billions of parameters or training computer vision algorithms, these GPUs deliver the computational power necessary for success.
But we go a step further by offering NVIDIA-certified reference architectures, designed in collaboration with NVIDIA to guarantee best practices. This ensures reliability and flexibility while allowing customisations tailored to your AI workloads. From GPU, CPU, and RAM configurations to firmware and middleware setups, we provide a finely tuned foundation ready for large-scale AI deployment without additional overhead.
Traditional air cooling methods are insufficient to manage the high power densities and performance demands of large-scale AI workloads. Currently, the average rack power density is approximately 15 kW/rack, but predictions estimate that AI workloads will push these demands to an astounding 60 to 120 kW/rack. Liquid cooling systems use the superior thermal conductivity of water or other liquids and offer a more efficient solution for high-density racks. These systems are up to 3,000 times more effective at heat transfer than traditional air cooling systems. At the AI Supercloud, we integrate liquid cooling solutions into our infrastructure. With liquid cooling, we ensure your systems can operate at their full potential, even under constant high-load operations.
Every AI project, from training massive neural networks to real-time inference relies on efficient and scalable storage solutions. AI workflows generate vast amounts of data from raw input datasets to processed outputs and model checkpoints. Inefficient storage systems can lead to bottlenecks, increased latency and prolonged training times, ultimately undermining the potential of even the most advanced GPUs.
To address this, we integrate NVIDIA-certified WEKA storage into our hardware solutions for high-performance AI environments. These storage systems provide:
AI workloads like inference require ultra-low latency and high-speed connectivity. For example, every millisecond counts in distributed AI training or real-time inference applications like autonomous driving. So, high-latency networks can create bottlenecks that compromise performance, no matter how advanced your GPUs or storage systems are.
We integrate NVIDIA Quantum-2 InfiniBand networking into our platform to ensure your workloads run with low latency. The NVIDIA Quantum-2 Infiband offers 400 Gb/s bandwidth per port and aggregate throughput of up to 51.2 terabits per second for advanced networking and high throughput.
Large-scale AI workloads demand far more than off-the-shelf hardware. They require an ecosystem optimised for maximum performance. For organisations looking to scale their AI operations in 2025, the AI Supercloud could be your ideal partner. Our infrastructure is fully optimised to support large-scale AI workloads, from training multi-billion parameter models or running real-time inference, our custom solutions ensure your AI projects run optimally.
Schedule a discovery call with our Solutions Engineer to assess your current infrastructure, business goals and specific needs to find the perfect solution for your AI projects.
Optimised GPUs are imperative because they provide the computational power needed for complex AI tasks, ensuring efficient parallel processing, scalability and precision required for training massive models and real-time inference.
Liquid cooling efficiently manages the high power densities of AI workloads by transferring heat 3,000 times more effectively than air cooling, maintaining system performance without overheating during constant high-load operations.
Efficient data storage is crucial for large-scale AI workloads because it ensures seamless access to vast amounts of data with high performance and low latency. Our NVIDIA-certified WEKA data storage eliminates data bottlenecks by offering exceptional performance, scalability, and low-latency data handling, making it perfect for high-demand AI workflows like training and real-time inference.
GPUDirect Storage allows GPUs to directly access data bypassing the CPU, reducing latency and speeding up data transfer to accelerate overall AI workload performance.
Yes, low-latency networking such as NVIDIA Quantum-2 InfiniBand is imperative for ensuring fast data transmission and minimal delays in distributed training and real-time AI workloads. This improves overall system responsiveness for smoother and faster performance for AI tasks.