IBM Launches Storage Server for AI Workloads

ESS 3500 is up to 12% faster than previous generation systems.

Ben Wodecki

May 25, 2022

1 Min Read
IBM Elastic Storage System 3500
IBM

IBM has unveiled the Elastic Storage System 3500 – a 2U storage server designed specifically for AI training workloads.

The new unit houses 24 drive bays and has a maximum raw capacity of 368TB of NVMe storage.

Making use of Spectrum Scale, IBM’s high-performance clustered file system software, the ESS 3500 can achieve up to 91GB/s of throughput.

The system is designed to "break performance barriers" for AI – with increased throughput enabling GPUs to solve AI problems faster.

The new offering can be combined with other IBM storage units – including previous entries in the ESS line. It is also compatible with non-IBM storage. Given its improvements to GPU output, it could be paired with Nvidia DGX A100 systems – which are GPU-focused but lack storage options.

The solution comes with Spectrum Scale installed on a pre-configured system, with updates delivered at speed through containerized software.

ESS 3500 key specs

System features

  • Dual 1-socket Storage Controllers, Active/Active

  • 1024 GB memory

  • De-Clustered RAID supporting erasure coding schemas: 3-way replication, 4-way replication, 4+2P, 4+3P, 8+2P, 8+3P

Performance

  • AMD 7642 48 core processor

  • Sequential read performance up to 91GB/S

Adapters

  • Four x16 PCIe Gen4 adapter slots

Drive support

  • 12 or 24 NVMe SSDs (3.84TB, 7.68 TB or 15.36 TB)

Software

  • ESS software 6.1.3.0

  • IBM Spectrum Scale for ESS 5.1.3.1

  • Red Hat Enterprise Linux (RHEL) 8.4

Full specifications can be found here.

This story originally appeared on AI Business, a Data Center Knowledge sister publication. To stay up-to-date with artificial intelligence news, subscribe to the AI Business newsletter.

About the Author

Ben Wodecki

AI Business

Ben Wodecki is assistant editor at AI Business, a publication dedicated to the latest trends in artificial intelligence.

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