In just the last few years, artificial intelligence (AI) has transformed from a cutting-edge concept to an integral part of various industries. AI spending in enterprises is continuing to increase, but supply chains are variable and with a spike in demand often comes a shortage in supply. Among the technologies most in demand: graphics processing units (GPUs), which have emerged as the workhorses of AI projects.
GPUs enable rapid parallel computations required for complex machine learning tasks. However, with soaring demand and a persistent shortage of GPUs, the right storage infrastructure to maximize GPU utilization has become paramount.
How Do GPUs Power AI Projects?
Originally designed for rendering images and graphics, GPUs have found an unexpected home in AI applications. This is due to their parallel processing capabilities, which allow them to handle multiple tasks simultaneously.
In AI projects, GPUs excel at performing the intricate calculations required by deep learning algorithms, which are at the heart of modern AI advancements. Tasks like training deep neural networks, which involve processing massive data sets and optimizing complex model architectures, require immense computational power. GPUs accelerate these computations significantly, reducing the time it takes to train models from weeks to mere hours.
This has profound implications for creative industries that heavily rely on AI-based algorithms, such as gaming, the film industry, and VFX studios. Think about the way things like special effects in movies used to come together before high-performance computing and GPUs: hundreds or even thousands of creative professionals working in tandem but largely by themselves on specific tasks (think workstations, animators, 3D modeling, etc.) and then having to bring it all together to see if what they did works in unison.
Now—with the power of GPUs, parallel processing, and generative AI—much of their work gets done by computers in an automated fashion, freeing up these creative professionals’ time to brainstorm new ideas, innovate, and focus on telling amazing stories.
The AI Data Conundrum and How Data Storage Can Help
While GPUs provide the computational muscle, data is the lifeblood of AI projects. The success of AI models hinges on the availability, quality, and volume of data. However, handling and managing these colossal data sets can be a daunting task. This is where storage comes into play.
An efficient and scalable storage solution is essential to ensure that data is accessible, manageable, and processed seamlessly by GPUs. Data needs to be fed to GPUs at high speeds to prevent computational bottlenecks, and this requires a storage infrastructure that can keep up with the pace of AI workloads.
How to Offset the GPU Shortage
Despite the undeniable importance of GPUs, industries and AI projects have been grappling with a persistent shortage of these hardware components. Factors such as increased demand, supply chain disruptions, and global events have contributed to the scarcity. This shortage has far-reaching consequences, as it impedes the progress of AI projects and hinders innovation across industries.
In this challenging landscape, organizations are compelled to optimize the utilization and performance of existing GPUs to achieve their AI goals—and data storage can help.
Optimizing GPU performance can help organizations make the most of their existing GPUs. However, performance enhancement isn’t solely about upgrading GPU hardware. Storage infrastructure plays a pivotal role in maximizing GPU utilization. High efficiency, high-speed, low-latency storage solutions ensure that data can be delivered to GPUs without delay, preventing them from idling while waiting for information. An efficient storage solution also allows for rapid model iteration, enabling AI engineers to experiment and fine-tune their models more quickly.
A Success Story: Chungbuk Technopark
One example of how the right storage can amplify GPU potential is the case of Chungbuk Technopark, a regional innovation hub that supports economic growth in Chungcheongbuk-do province of South Korea. Facing resource constraints due to the GPU shortage, Chungbuk turned to Pure Storage for a high-performance storage infrastructure.
With this new infrastructure, Chungbuk achieved faster data access times for its AI workloads. This resulted in improved GPU utilization and accelerated model training times, ultimately enabling the organization to achieve its AI objectives and a twofold increase in storage data processing for faster AI performance.
The Future of AI Success is Data Storage
Looking ahead, it’s clear that the role of data storage in AI success will only become more crucial. As AI projects scale and become more sophisticated, the demand for computational power and storage capacity will surge. Organizations that invest in robust and efficient storage optimized for AI will be better equipped to handle these demands and continue their AI journey uninterrupted.
AI initiatives will heavily rely on the synergy between GPUs and storage infrastructure, and a well-optimized combination will be the key to unlocking AI’s full potential.
The Pure Storage-NVIDIA Partnership
Recognizing the tightly intertwined relationship between storage and GPUs in AI projects, Pure Storage has forged a strategic partnership with NVIDIA, a leading provider of GPUs and AI computing solutions. This partnership is designed to deliver seamless integration between Pure Storage’s high-performance storage solutions and NVIDIA’s powerful GPUs.
By aligning technologies, Pure Storage and NVIDIA aim to provide organizations with a comprehensive platform that enhances AI workloads, maximizes GPU utilization, and accelerates time to insight. AI Ready Infrastructure (AIRI) combines the latest AI compute, networking, storage and software components from Pure Storage and NVIDIA in a tested and validated reference architecture to accelerate deployment and reduce risk when compared to alternatives. With features like high throughput, low latency, and non-disruptive scalability, Pure Storage’s solutions are tailor-made to complement GPU-driven AI projects.
In the era of AI expansion, GPUs have emerged as indispensable tools for driving AI projects forward. However, the current shortage of GPUs has highlighted the importance of maximizing their utilization to meet the demands of AI workloads. By leveraging Pure Storage’s technology, organizations can ensure that their AI initiatives run smoothly, even in the face of GPU shortages.
With their combined expertise, Pure Storage and NVIDIA will play a pivotal role in shaping the future of AI success.