Why storage solutions for AI are suddenly so important

Floyd Christofferson
Aug 7
3 min read

Artificial intelligence (AI) and deep learning (DL) are revolutionizing numerous industries, from manufacturing to medicine. A central problem facing many AI projects is the need to process enormous amounts of data quickly and reliably. This becomes particularly challenging when this data is unstructured. This means it is not stored in traditional tables, but rather in images, text documents, or sensor data, for example.

This is where the Parallel Network File System (pNFS) version 4.2 comes in. This evolution of an established storage protocol was originally used in traditional IT environments.

This new version offers many advantages that AI applications need today:

Fast access to data
Scalability for large amounts of data
Compatibility with existing IT infrastructure

Abstract visualization of a digital data stream in shades of blue and turquoise. Luminous lines run horizontally through the image, symbolizing parallel data processing and network traffic. — Visualize parallel data streams – pNFS v4.2 enables fast access and efficient scaling for data-intensive AI workloads.

According to Fortune Business Insights, the global high-performance computing market will grow from USD 54.39 billion in 2024 to USD 109.99 billion by 2032. This growth clearly demonstrates that companies seeking to optimally run AI and data applications rely on scalable and open infrastructures—exactly what pNFS v4.2 provides.

What is pNFS? A basic explanation

What is NFS?

NFS (Network File System) is an open protocol that allows computers to access shared files over a network. This works similarly to a shared hard drive.

The evolution to pNFS

pNFS (Parallel NFS) is the modern version of NFS. Unlike previous systems, which routed all data requests through a single server, it allows multiple servers to be accessed simultaneously. This saves time, prevents bottlenecks, and increases reliability.

Advantages of pNFS version 4.2

Version 4.2 of pNFS brings additional benefits, including:

Flex Files for intelligent data distribution
Efficient handling of metadata, e.g., information about files.

Relevance of pNFS for AI applications

Many traditional NAS and file systems quickly reach their limits when it comes to AI workloads. The following common problems arise, which pNFS v4.2 specifically addresses:

| Typical problem | Solution with pNFS v4.2 |

| ------------------ | ---------------------- |

| Data and metadata run along the same path → risk of congestion | Separation of access paths → less latency, more speed |

| Many small file operations (e.g., during AI training) overload the system | Client-side caching → reduction of metadata traffic by up to 80% |

| A single network connection limits data throughput | N-Connect → Multiple TCP connections per access → Higher performance & stability |

| Proprietary storage solutions are expensive and inflexible | Open protocol → Works with existing NAS infrastructure |

| Static data distribution slows down dynamic processes | Flex Files → Data distribution via striping, mirroring, and in the future also erasure coding |

The five biggest advantages of pNFS v4.2

1. Work faster with parallel access

Parallel access to data significantly increases throughput. Instead of using a single central access point, multiple data streams can be processed simultaneously.

2. Less data congestion

By storing metadata locally, the computer doesn't have to constantly query this information, helping to reduce overall data traffic.

3. More bandwidth

With "N-Connect," multiple network connections can be used simultaneously. This is equivalent to using multiple highways for fast data transfer, perfect for data-intensive applications.

4. Easy integration

pNFS requires no special hardware. It can be easily used with existing systems that support NFSv3.

5. Flexibility for modern workloads

AI pipelines are dynamic. Therefore, pNFS with Flex Files enables flexible data distribution, whether for security purposes or to optimize performance.

Target groups for pNFS v4.2

pNFS v4.2 is particularly interesting for:

Companies with growing data volumes
Research institutions conducting AI projects
IT teams that want to use existing storage resources more efficiently
Organizations with distributed data centers or multiple locations

Advantages at a glance:

Increase performance without new hardware
Open standard, no vendor lock-in
Compatible with existing systems
Scalable up to the petabyte range
Optimal for Linux environments

Conclusion: A modern storage protocol for data-driven innovation

pNFS v4.2 is more than just a technical upgrade. It represents a bridge between traditional IT infrastructure and modern AI applications . Anyone looking to efficiently process large amounts of data should definitely consider this solution.

For in-depth technical details and application examples, you can find a comprehensive analysis in the Hammerspace whitepaper (external) .

About the author

Floyd Christofferson is VP of Product Marketing at Hammerspace . He has extensive experience in storage architectures, data management, and the development of open infrastructure standards. His focus is on scalable solutions for data-intensive workloads in the context of AI and research.