Once thought to be useful only for rendering video games, GPUs now power some of the world's most important computational workloads. Global spending on data centre GPUs will reach USD 63 Billion by 2028. Whether it be training deep learning algorithms, scientific simulations, financial modelling or real-time analytics, GPUs enable data centres to derive insights, automate decisions and innovate products in minutes rather than months.
A recent survey from AMD reported that over 75% of IT leaders witnessed increasing AI investment, with 90% already having significant returns. So, this reliance presents an opportunity for organisations to stay ahead. By understanding the capabilities of data centre GPUs, organisations can accelerate decision-making, transform customer experiences and keep pace with the trends. The question is no longer whether to consider data centre GPUs but rather how to integrate them most effectively.
Data centre GPUs refer to specialised graphics processing units designed for deployment in data centre environments. Unlike consumer-grade GPUs primarily used for gaming or personal computing, data centre GPUs are optimised for heavy-duty computational tasks. They are designed to meet the demands of enterprise-level applications such as Artificial Intelligence, Machine Learning, High-Performance Computing, Data Analytics and Scientific Simulations.
These GPUs are developed with features that prioritise performance, reliability and scalability in large-scale server deployments. They typically feature higher memory capacities, greater computational power, and optimised software support for parallel processing tasks.
Using Data Centre GPUs offers numerous advantages, especially for tasks that require high levels of parallel processing power. Data centre GPUs often incorporate technologies like error correction mechanisms and improved cooling solutions to ensure uninterrupted operation under demanding workloads.
In AI and machine learning applications, where complex mathematical calculations are prevalent, GPUs excel at matrix and vector operations. These operations are fundamental to training neural networks and running sophisticated AI models efficiently. By leveraging the parallel processing capabilities of GPUs, data centres can process vast amounts of data at unprecedented speeds, enabling quicker insights and decision-making processes.
The utilisation of data centre GPUs is particularly beneficial for high-performance computing tasks that involve computationally intensive simulations, such as weather forecasting, financial modelling and scientific research. The ability of GPUs to handle a large number of small calculations simultaneously makes them indispensable for accelerating HPC workloads and achieving faster results.
Energy efficiency is a critical consideration in data centre operations due to the substantial power consumption associated with running servers and cooling systems. While GPUs consume more power than CPUs, they offer superior energy efficiency for specific tasks that benefit from parallel processing. This efficiency stems from the ability of GPUs to complete computations faster than CPUs by distributing workloads across numerous cores.
The reduced heat generation of GPUs compared to CPUs can positively impact cooling management in data centres. Heat dissipation is a significant challenge in large-scale data centres, as excessive heat can lead to equipment failures and increased cooling costs. By incorporating GPUs into data centre infrastructure, organisations can mitigate heat-related issues, optimise cooling systems and ultimately reduce energy consumption.
GPUs can be leveraged to accelerate network operations by improving communication between servers and reducing latency. This acceleration is important for optimising network performance, especially in scenarios where real-time data processing is essential.
Offloading network processing tasks from CPUs to GPUs can streamline data centre networking operations. By relieving CPUs of network-related responsibilities, organisations can optimise resource allocation, and improve system performance and overall operational efficiency. This offloading mechanism enables CPUs to focus on core computing tasks while ensuring that networking functions are efficiently managed by dedicated GPU resources.
AI workloads are different from traditional enterprise workloads, requiring specialised hardware and software configurations to perform optimally. This has led to the rise of AI-ready data centres, which are purpose-built facilities designed to support the requirements of AI applications.
AI workloads are highly compute-intensive, requiring massive processing power to train and run complex machine-learning models. These workloads often involve large datasets and intricate neural networks, which necessitate significant computational resources, including high-performance GPUs and specialised AI accelerators.
GPUs have become the preferred hardware for AI workloads due to their ability to perform parallel processing operations efficiently. They are designed to handle the matrix and tensor computations that are essential for deep learning and other AI tasks. AI accelerators, such as Google's Tensor Processing Units (TPUs) and Intel's Nervana Neural Network Processors (NNPs) are purpose-built for AI workloads and offer even higher performance and energy efficiency than traditional GPUs.
In addition to hardware requirements, GPU intensive workloads also demand specific software configurations. This includes deep learning frameworks like TensorFlow and PyTorch as well as specialised libraries and tools for data preprocessing, model training, and inference. AI-ready data centres are designed to integrate these software components, ensuring optimal performance and compatibility with various AI applications.
Partnering with an AI-ready data centre provider offers several benefits for organisations looking to leverage AI technologies:
Our partnership with AQ Compute to advance towards a Net Zero AI Supercloud presents a compelling opportunity for organisations seeking cutting-edge data centre GPU solutions. The AI Supercloud project aims to democratise AI innovation by providing on-demand access to some of the world's most powerful GPUs. This initiative aims to break down barriers to entry for organisations looking to adopt and advance their enterprise AI capabilities. By partnering with NexGen Cloud, you can tap into a vast pool of GPU resources that were previously inaccessible due to cost and complexity constraints.
“The AI market is expected the grow exponentially over the next 12-24 months and as such it is imperative to decarbonise as much of the data generated as possible. By housing the data created within our AI Supercloud in AQ Compute data centres, we are not just innovating technologically, we are pioneering a sustainable shift in the way AI data is processed. We are not only the first to be doing this in Europe but hope to set the industry standard at the same time.”
- Chris Starkey, CEO, NexGen Cloud
The surge in demand for data centre resources, particularly GPUs for AI workloads, necessitates scalable solutions that can adapt to evolving requirements. Our commitment to providing scalable and flexible GPU services ensures that organisations can seamlessly scale their AI initiatives without being limited by resource constraints. This scalability enables businesses to keep pace with the rapid advancements in AI technology and innovation.
Our focus on sustainable power management aligns with environmental goals and efficient energy usage. By building one of the largest fleets of GPUs powered entirely by renewable energy sources, NexGen Cloud demonstrates a commitment to reducing carbon footprints and operating in an environmentally responsible manner. Choosing our data centre GPUs not only ensures high performance but also contributes to a more sustainable future.
Our other partnership with WEKA brings unparalleled performance and scale to the AI Supercloud project. The WEKA Data Platform software optimises data access and processing for large-scale GPU deployments, resulting in up to 20x faster performance. This enhanced efficiency allows you to focus on developing algorithms and driving innovation without being hindered by underlying data architecture challenges.
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Data Centre GPUs are high-performance graphics processing units specifically designed for deployment in data centres. These GPUs are optimised for parallel processing and accelerating computational workloads like AI, machine learning, scientific computing, and data analytics.
Using GPUs in Data Centres offers several key benefits: accelerated performance for parallel workloads, energy efficiency with higher FLOPS/watt, specialised processing cores tailored for AI/ML tasks and the ability to scale up AI/deep learning models.
Partnering with AI-ready data centres offers scalability and top-notch performance to handle demanding AI workloads. It optimises costs by providing enterprise-grade infrastructure without significant upfront investments. These data centres ensure robust security measures and compliance with industry regulations to protect sensitive data and AI models. They offer flexible deployment options like on-premises, cloud, or hybrid solutions, enabling organisations to choose the best fit for their needs and requirements, allowing for greater agility and responsiveness to changing business demands.