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Improving Application Performance with NVMe Storage - Part 3

NVMe Storage Use Cases and Summary: Benefits of NVMe storage for AI/ML
Zivan Ori

Start with Part 1: The Rise of AI and ML Driving Parallel Computing Requirements

Start with Part 2: Local versus Shared Storage for Artificial Intelligence (AI) and Machine Learning (ML)

NVMe Storage Use Cases

NVMe storage's strong performance, combined with the capacity and data availability benefits of shared NVMe storage over local SSD, makes it a strong solution for AI / ML infrastructures of any size. There are several AI / ML focused use cases to highlight.

■ Financial Analytics – Financial services and financial technology (FinTech) are increasingly turning to automation and artificial intelligence to fuel their decision making processes for investments. Using a mix of historical data and financial modeling, one platform can provide the horsepower required for predicting future investment strategies for their financial customers.

■ Image Recognition in Manufacturing – Manufacturing has long used automation in their production lines to increase the output capacity of their production systems, scaling from hundreds of units to thousands or even millions of units per hour. The financial impact of a quality issue on the production line can be devastating if not caught in a timely manner. Real-time image recognition of photos of manufactured parts is essential to determining whether a part meets the quality standards required, as well as capturing systematic quality issues in real-time.

■ Car Services – Ride sharing apps have given rise to a new paradigm in public transit, allowing users and drivers to connect quickly and easily as needed. Ride sharing companies use AI / ML for traffic modeling to position drivers where they are most needed based on both past and current ride sharing requests. This increases the drivers' potential revenue by reducing drive times as well as increases customer satisfaction through reduced wait times, both of which improve the revenue potential for the ride sharing company.

Beyond AI / ML, one vendor also provides more generalized computing services for their customers. They provide storage capacity for cloud services, using OpenStack and Kubernetes in conjunction with NVMe storage for high performance storage. In addition, they also leverage NVMe storage for big data analytics, using spark applications to perform multiple types of data analytics tasks, such as SQL, data mining and more.

Summary: Benefits of NVMe storage for AI/ML

NVMe storage is an ideal solution for countless AI / ML workloads, especially machine learning for multiple applications. With NVMe storage, you can:

■ Create and manage larger shared data-sets for training – By separating out storage capacity from the compute nodes, data-sets for machine learning training can scale up to 1PB. As the data-set grows and more NVMe storage is brought online, performance grows as well, rather than being limited by legacy storage controller bottlenecks.

■ Overcome the capacity limitations of local SSDs in GPU nodes – With limited space for SSD media, GPU nodes have limited capacity to manage larger datasets. With NVMe storage, NVMe volumes can be dynamically provisioned over high performance Ethernet or InfiniBand networks.

■ Accelerate epoch time of machine learning by as much as 10x – By leveraging high performance NVMe-oF, NVMe storage eliminates the latency bottlenecks of older storage protocols and unleashes the parallelism inherent to the NVMe protocol. Every GPU node has direct, parallel access to the media at the lowest possible latency.

■ Improve the utilization of GPUs – Having GPUs rest idle due to slow access to data for processing is costly. By offloading storage access to the idle CPUs, and delivering storage performance at the speed of local SSD, NVMe storage ensures that the GPU-nodes are kept busy with fast access to data.

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According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

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Improving Application Performance with NVMe Storage - Part 3

NVMe Storage Use Cases and Summary: Benefits of NVMe storage for AI/ML
Zivan Ori

Start with Part 1: The Rise of AI and ML Driving Parallel Computing Requirements

Start with Part 2: Local versus Shared Storage for Artificial Intelligence (AI) and Machine Learning (ML)

NVMe Storage Use Cases

NVMe storage's strong performance, combined with the capacity and data availability benefits of shared NVMe storage over local SSD, makes it a strong solution for AI / ML infrastructures of any size. There are several AI / ML focused use cases to highlight.

■ Financial Analytics – Financial services and financial technology (FinTech) are increasingly turning to automation and artificial intelligence to fuel their decision making processes for investments. Using a mix of historical data and financial modeling, one platform can provide the horsepower required for predicting future investment strategies for their financial customers.

■ Image Recognition in Manufacturing – Manufacturing has long used automation in their production lines to increase the output capacity of their production systems, scaling from hundreds of units to thousands or even millions of units per hour. The financial impact of a quality issue on the production line can be devastating if not caught in a timely manner. Real-time image recognition of photos of manufactured parts is essential to determining whether a part meets the quality standards required, as well as capturing systematic quality issues in real-time.

■ Car Services – Ride sharing apps have given rise to a new paradigm in public transit, allowing users and drivers to connect quickly and easily as needed. Ride sharing companies use AI / ML for traffic modeling to position drivers where they are most needed based on both past and current ride sharing requests. This increases the drivers' potential revenue by reducing drive times as well as increases customer satisfaction through reduced wait times, both of which improve the revenue potential for the ride sharing company.

Beyond AI / ML, one vendor also provides more generalized computing services for their customers. They provide storage capacity for cloud services, using OpenStack and Kubernetes in conjunction with NVMe storage for high performance storage. In addition, they also leverage NVMe storage for big data analytics, using spark applications to perform multiple types of data analytics tasks, such as SQL, data mining and more.

Summary: Benefits of NVMe storage for AI/ML

NVMe storage is an ideal solution for countless AI / ML workloads, especially machine learning for multiple applications. With NVMe storage, you can:

■ Create and manage larger shared data-sets for training – By separating out storage capacity from the compute nodes, data-sets for machine learning training can scale up to 1PB. As the data-set grows and more NVMe storage is brought online, performance grows as well, rather than being limited by legacy storage controller bottlenecks.

■ Overcome the capacity limitations of local SSDs in GPU nodes – With limited space for SSD media, GPU nodes have limited capacity to manage larger datasets. With NVMe storage, NVMe volumes can be dynamically provisioned over high performance Ethernet or InfiniBand networks.

■ Accelerate epoch time of machine learning by as much as 10x – By leveraging high performance NVMe-oF, NVMe storage eliminates the latency bottlenecks of older storage protocols and unleashes the parallelism inherent to the NVMe protocol. Every GPU node has direct, parallel access to the media at the lowest possible latency.

■ Improve the utilization of GPUs – Having GPUs rest idle due to slow access to data for processing is costly. By offloading storage access to the idle CPUs, and delivering storage performance at the speed of local SSD, NVMe storage ensures that the GPU-nodes are kept busy with fast access to data.

The Latest

According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

Image
Broadcom

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...