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Whether you are on the business side of the house or deeply entrenched in IT, there’s a good chance you were at least intrigued, if not taken aback, by the introduction of ChatGPT a little over a year ago.

Yeah, we knew that AI was rapidly evolving and that something like this was coming. But when we actually heard about – or directly witnessed – the types of queries to which it could respond, the amount of data it was able to gather and the quality of the information presented (very good, though not always 100% cogent) it was a remarkable milestone. What astonished most of us was the speed with which ChatGPT, powered by accelerated computing, generated results.

At 8:00 AM you ask an AI system to produce a report that predicts the quantitative economic impact of AI on each of the G20 nations by the year 2030. At 8:15 AM you receive an intelligently written, grammatically correct, well-researched, 10-page report with detailed analysis, charts and an annotated bibliography. You can’t help but be impressed – and maybe a little worried. More so when you realize this effort might have taken a small team of economists and data scientists days or weeks to pull together. Of course, accuracy is important…we know that part has to get better, and it certainly will. Extensive training is still needed to realize the promise of many AI systems, regardless of their output.

But speed is really the name of this AI game, isn’t it? Everything is about accelerated results. Faster ROI on AI investments and shorter time to market for AI-enabled products are just part of the pay-off in operational speed.

The Power of GPUs: Elevating AI & Reducing Costs

The connection between speed (let’s say accelerated computing) and operational efficiency is indisputable. It’s not just the obvious things like how fast projects get completed or products get to market when AI is used. It’s also the impact of AI on data center efficiency… we’re talking about savings on things like hardware, energy, floor space and operating costs. It may not be intuitive, but getting AI systems to run faster actually makes data centers more energy- and cost-efficient – on-premises and in the cloud.

Again, whether you’re on the business side or the IT side, you’ve become familiar with companies like NVIDIA, the power of GPUs and their crucial role in taking AI mainstream. You may have also heard NVIDIA’s CEO, Jensen Huang, state that “Accelerated computing is the best way to do more with less”.

What he’s getting at is the fact that GPU-based systems can accelerate processing by up to 50X compared to CPU-based systems. The implication is that tens of thousands of CPU-based servers can now be replaced by a few hundred GPU-based systems. Given that about 55 percent of data center energy today is used to power hardware systems, such as servers and storage, and over 40 percent is used to cool these resources, such a reduction in compute resources would slash data center energy costs and space requirements considerably. Additionally, while storage is typically a small fraction of the overall power requirement – a storage system’s capabilities can have a huge knock-on effect.

AI Data Management Accelerated with DDN A³I & EXAScaler

AI Infrastructure Architects and IT teams more broadly are also beginning to realize the importance of AI storage in enabling accelerated computing. They understand that GPU-based systems run faster when paired with parallel file systems that offer super-fast data ingest and can fully saturate GPUs to maximize both AI performance and resource utilization.

This type of parallel data management architecture, as seen in DDN’s A³I appliances, enables efficient LLM offloading from GPUs and delivers throughput that greatly outperforms NFS and other enterprise storage solutions. DDN’s optimized IO processes can accelerate GPU performance by 10X versus our closest competitors.

In fact, DDN systems deliver the fastest and most responsive small IO, random IO and metadata performance in the market – all critical for AI models. At the same time, the architecture can scale performance linearly to hundreds of petabytes to support the latest LLMs that might juggle billions of parameters. The underlying EXAScaler parallel file system uniquely addresses the explosive demand for performance-hungry GPU- and DPU-based environments, processing AI data from diverse sources ranging from numerical models to high-resolution sensors.

The reality is that DDN powers more AI systems, representing more than 250,000 GPUs, across more markets than any other storage vendor in the world and is instrumental in improving data center efficiency via accelerated GPU performance.

Optimizing AI Performance: Power and Storage Improvements

So far we’ve talked about how DDN storage helps GPUs run faster. We should note that DDN technologies help accelerate all layers of the AI stack, networks and of course, filesystems and storage media – all of which have an impact on data center efficiency.

Let’s step down a notch and talk about some of the energy- and space-saving benefits that DDN storage delivers directly for Generative AI and LLM environments. This includes accelerated training for the largest and most complex LLM frameworks in the world, enabling transformer models like GPT, Bert, and Megatron LM.

  • DDN’s EXAScaler parallel file system can drive 10X to 15X more data per watt, delivering outstanding results using only a fraction of the power and rack space of conventional storage systems.
  • Machine learning is both read- AND write-intensive. DDN delivers 30X faster random read and write throughput than our competitors. EXAScaler systems deliver the best IOPS and throughput in the industry per rack – up to 70M IOPS and 1.8T/s and 1.4TB/s of read and write throughout, respectively.
  • We offer the best compression and data reduction performance. And because data is compressed directly from the application, less data moves over the wire for reads and writes.
  • Our systems reduce storage wait times for data loads by 4X.
  • Thousands of checkpoints are required for AI. Due to far superior write speed, ours run 15X faster, slashing training cycle run time.
  • DDN’s Hot Nodes feature automatically caches data sets on internal NVMe devices. This greatly reduced network and storage load during multi-epoch AI training.
  • These efficiencies reduce data center runtime by 5% to 12%, delivering faster training and higher productivity to LLMs and Generative AI.
  • Essentially, for every $1 you spend on DDN storage, you can recoup $2 in infrastructure productivity.

Elevate Your AI Journey with DDN

So, you now have a little more information about the connections from storage to accelerated computing to data center efficiency; a relationship which will become increasingly evident and stronger as LLMs get even larger, and AI becomes more ubiquitous.

Yes, the replacement of countless CPU-based systems with far fewer, more powerful GPUs and DPUs can be expected to greatly reduce energy and space consumption for many organizations. However, the optimal energy and space efficiency of accelerated computing can only be realized when it includes a complementary data management platform. Today this platform requires a parallel storage architecture like DDN EXAScaler that can process data for large, highly complex AI models, fully unleash the power of GPU-based systems and provide innovative storage-driven performance and space-saving advantages across the AI stack.

Embark on a journey to unparalleled efficiency in your GPU deployments and discover which leading GPU Cloud vendors trust DDN to supercharge their operations. If you’re ready to unlock the full potential of your AI and computing infrastructure with the industry’s most advanced storage solutions, it’s time to take action. Connect with us today, and let’s explore how DDN can transform your technological landscape, propelling you into a future of unmatched performance and efficiency.

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