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AI WORKLOADS POSE NEW CHALLENGES TO YOUR STORAGE INFRASTRUCTURE


BY MICHAEL MCNERNEY, VICE PRESIDENT MARKETING AND NETWORK SECURITY, SUPERMICRO.

 * 1 month ago Posted in

There’s a complete makeover underway in modern enterprises today. It’s centered
around what might be called the “AI revolution.” Organizations are obtaining
competitive advantages and key insights when they put advanced AI- or ML-based
applications to work.


Among the leading examples of such workloads are large language models (LLMs)
such as ChatGPT along with ML models based on humongous training data sets,
complex 3D models, animation and virtual reality, simulations, and other data-
and compute-intensive applications.

For a high performance AI or ML solution, an entire, comprehensive set of
hardware needs to be working together. Behind the flashy rack-mounted hardware
that houses the GPU-driven brains of any AI cluster, high throughput,
low-latency storage systems are needed to keep the cluster productive. These
support the I/O channels feeding massive amounts of data to train models and
perform complex simulations and analyses needed to support AI, ML, and similar
workloads. Indeed, one of the biggest challenges facing businesses looking to
capitalize on the growth of AI is finding a storage solution that won’t
bottleneck their high-performance CPUs, GPUs, or database clusters. After all,
the CPUs and GPUs need to be kept busy to reduce the TCO of the data center.

Avoiding storage bottlenecks

What happens in distributed and parallel modes is that for the distributed file
systems, data arrives from multiple sources where that data needs to get
processed at scale across various protocols and for various applications. In a
typical storage system, metadata quickly becomes a bottleneck. Indeed, you can
only pump as much data through the system as the metadata supports. As the
amount of data scales, the ability to handle metadata needs to scale
proportionally.

WEKA distributed storage is architected to provide such proportional scaling.
That explains why, despite adding more data capacity to a system or cluster, the
I/O performance continues to scale linearly from eight (minimum node count for a
WEKA cluster) to hundreds of storage nodes. It does so by eliminating
bottlenecks and providing support for even the heaviest and most demanding AI/ML
(and other similar) workloads.

But there’s more to optimizing servers and clusters than providing scalable,
high-performance, low-latency storage. When designing an entire system, the
focus cannot be exclusively on any single feature or function. The entire
architecture must work in concert to support targeted workloads. Thus, designing
a system for AI applications means creating a runtime environment built from the
ground up to handle data-intensive applications both quickly and satisfactorily.
What the server does with the data while handling an AI workload is as essential
as the data traffic into and out of any given node.

NVMe to the rescue

Another critical feature is the number of PCIe 5.0 lanes. This technology
enables servers to accommodate more extensive collections of SSDs, NICs, GPUs,
and even extended memory CXL devices. All of these play essential roles in
handling demanding AI and ML workloads, including PCIe Gen5 SSDs for high-speed
local storage, large numbers of high-speed network interfaces to connect servers
to other nodes, such as storage or other specialized servers, to extend data
scope and reach as well as large numbers of GPUs for handling specialized,
targeted tasks or workloads.

NVMe devices have totally changed the server and cluster game. With NVMe at its
base, a completely reworked architecture becomes possible. It allows storage to
work at scale and speed alongside high-performance CPUs, GPUs, and NICs,
especially with the EDSFF form factor. Single-socket design enables
best-of-breed CPUs to fully saturate network cards and storage and exploit the
highest possible levels of parallelism and clustering capabilities for HPC, AI,
and other next-generation solutions.

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AI AND DATA CENTRES - WHY AI IS SO RESOURCE HUNGRY

By Ed Ansett, Founder and Chairman, i3 Solutions Group.


AI STRATEGIES: LEARNING FROM THE LAST ‘GOLD RUSH’ TO CLOUD

By Chris Jackson, Chief Product and Technology Officer at Six Degrees.


HOW ECOMMERCE AND FINTECH FIRMS ARE LEVERAGING THE TRANSFORMATIVE POWER OF
AI-ENABLED DATA CENTRES

Andy Connor, EMEA Channel Director at Subzero Engineering, examines the impact
AI, blockchain and process automation is having on the eCommerce and fintech
sectors.


AI – AN INDUSTRY SNAPSHOT

By James Carmillet, Director of Cost Management at BCS.



THE AI BURDEN WILL BE MET BY DATACENTRES. CAN THEY COPE?

By Nicholas Cole, Data Centre Solution Manager, EXFO.


MASTER AI BEFORE AI MASTERS YOUR BUSINESS

By Michael Deheneffe, Director Marketing & Innovation at Orange Business
Services, Digital & Data.


THE EMPOWERING ROLE OF AI IN MARKETING

By Jess Petrella, Director of Product Marketing at Unbounce.


COULD EDGE COMPUTING UNLOCK AI’S VAST POTENTIAL?

By Wayne Carter, VP Engineering, Couchbase.


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