By Tarun Dua, Managing Director at E2E Networks Ltd
Introduction
With the increasing demand for artificial intelligence (AI) and machine learning (ML), organisations and researchers are relying on cloud GPU infrastructure as a service (IaaS) to fulfill their computational requirements. International Data Corporation (IDC) reports that the Indian AI market is projected to experience a compound annual growth rate (CAGR) of 20% and reach $7.8 billion by 2025. As of 2022, India had already generated $12.3 billion in AI revenue, indicating significant potential for further growth in the country.
Furthermore, the AI software market, encompassing platforms, solutions, and services, is expected to expand at a CAGR of 18% by the end of 2025.
The success of an AI and ML workflow heavily relies on robust computational power and advanced hardware infrastructure, which can be quite expensive to acquire and maintain. This is where Cloud GPU Infrastructure as a Service (IaaS) comes into play, offering a scalable and cost-effective solution for organisations looking to experiment and scale their AI and ML workloads.
A report from Future Market Insights (FMI) indicates that the global demand for GPU as a service is anticipated to experience substantial growth throughout the forecast period from 2022 to 2032. With a projected compound annual growth rate (CAGR) of 40%, the market is expected to reach a significant milestone of US$80.99 billion by 2032. This highlights the increasing adoption and utilisation of GPU as a service, reflecting its growing importance in various industries and applications worldwide.
Cloud GPU Infrastructure as a Service (IaaS) for Efficient AI and ML Workflows:
Cloud GPU Infrastructure as a Service refers to the provision of GPU-powered virtual machines and dedicated GPU instances in the cloud. It allows businesses to access high-performance computing resources, such as graphics processing units (GPUs), on-demand, without the need for upfront capital investment or the burden of managing complex hardware setups. Cloud providers offer a wide range of GPU options, including the latest generation of NVIDIA GPUs, which are specifically designed for accelerating AI and ML workloads.
Yet, while discussions on GPU Cloud IaaS often revolve around GPU models, several factors such as PCI4/PCI5, NVLink, NVMe, CPUs generation, and network performance are often neglected. This article aims to delve into the importance of these elements in facilitating efficient AI and ML workflows for businesses, and their influence on the overall performance.
Key Considerations for Choosing the Right Cloud GPU Infrastructure Provider
When choosing the right Cloud GPU infrastructure provider, businesses should ensure that the provider has created a well-balanced and optimised GPU infrastructure backbone that fully utilises the computational power of GPUs, resulting in improved AI and ML workflows and enhanced training and algorithmic performance.
PCI4/PCI5 Bandwidth
When incorporating GPUs into cloud infrastructure, it becomes imperative to ensure a harmonised bandwidth between the GPU instance and its corresponding PCI4/PCI5 interface.
PCIe (Peripheral Component Interconnect Express) is a widely used high-speed serial bus standard that connects various components within a computer system. GPUs, as well as other devices like network cards and storage controllers, utilise PCIe interfaces to communicate with the CPU and transfer data.
GPUs heavily rely on high-speed data transfer to achieve peak performance. By employing GPU Cloud Infrastructure providers which specialise in providing GPUs equipped with suitable PCI4/PCI5 interfaces, organisations can unlock the maximum capabilities of their GPUs, while simultaneously preventing any bottlenecks that might impede computational throughput.
NVLink for Enhanced GPU Communication
NVLink is a high-speed interconnect technology specifically designed for NVIDIA GPUs. It enables direct communication between multiple GPUs, allowing them to work together seamlessly and share data faster.
By ensuring that GPU Cloud Infrastructure providers leverage NVLink in their GPU infrastructure, businesses can more efficiently execute complex AI and ML workloads that require large-scale parallel processing. It enables direct communication between GPUs, bypassing the CPU and allowing them to work together efficiently on parallel computing tasks. NVLink is designed to overcome the limitations of traditional PCIe-based communication and provide significantly higher bandwidth and lower latency.
Faster Storage with NVMe
In AI and ML workflows, data access speed plays a crucial role in overall system performance. Traditional storage solutions often become a bottleneck, limiting the GPU’s ability to process data efficiently.
By ensuring the GPU Cloud infrastructure provider harnesses NVMe (Non-Volatile Memory Express) storage, businesses can leverage the lightning-fast data transfer capabilities of NVMe, reducing data retrieval latency and improving performance.
Latest Generation CPUs
While GPUs are at the heart of AI and ML computations, CPUs also play a vital role in orchestrating and managing these workloads. The latest generation CPUs offer improved performance, better energy efficiency, and advanced instruction sets that can further optimise the execution of AI and ML tasks.
Ensuring that the provider’s GPU infrastructure is equipped with the latest CPUs can lead to significant performance gains and better resource utilisation. They feature higher clock speeds, increased core counts, improved instructions per clock (IPC), and architectural enhancements that optimise computation.
Network Performance Optimisation
In distributed AI and ML environments, where multiple GPUs and CPUs collaborate on a single task, network performance becomes a critical factor. Efficient data exchange and communication between different computing resources are essential for achieving optimal results.
High-speed, low-latency networking technologies can reduce data transfer times and enable real-time collaboration among distributed resources, enhancing the scalability and efficiency of AI and ML workflows.
Cloud GPU IaaS providers who ensure optimised network performance show better performance and efficiency on AI / ML workloads, even if GPU models might be similar.
Conclusion
When choosing the right GPU Cloud IaaS, many businesses tend to overlook the critical aspects of PCI4/PCI5 bandwidth, NVLink capabilities, NVMe storage, CPU capabilities, and network performance as elements that can impact efficiency and overtly focus only on the GPU model itself. This, however, often leads to lower than optimal performance, as the capability of the GPU in delivering ML workloads depends on the components that empower it.
By exploring providers who optimise for these factors, businesses and researchers can ensure that they end up choosing the right IaaS provider for their workloads and harness the full potential of GPUs, achieve faster training and inference times, improve scalability, and ultimately enhance the overall outcomes of their AI and ML projects. It is, therefore, essential to raise awareness and emphasise the significance of these often overlooked elements when discussing GPUs and their impact on AI and ML workflows.
