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GIGABYTE refreshes AMD EPYC server line-up

GIGABYTE, a provider of high performance server motherboards and systems, has refreshed its AMD EPYC 1U and 2U server line-up. 

The updated range supports different storage device combinations, with increased NVMe connectivity to integrate dense configurations of high bandwidth storage.

These five systems are part of GIGABYTE’s ready-to-integrate general purpose Rack Server family, equipped with the high performance power supplies and cooling fans and combining performance, energy efficiency and overall reliability for web hosting, mass storage, virtualised infrastructures, analytics and other demanding applications.

GIGABYTE’s AMD EPYC server systems are based on the 7000 series EPYC processor, offered as a SoC and incorporating a multi-die design with 32 cores per processor, 128 PCIe lanes and 8 channels of DDR4 memory.

These features have allowed GIGABYTE to create a range of servers that pack a real punch in flexibility and expansion options. First released in July last year, adoption of GIGABYTE’s AMD EPYC servers has been gaining momentum, lowering TCO for datacentres by offering an optional balance of compute, memory, I/O and security.

The five refreshed GIGABYTE AMD EPYC server products are the 1U form factor R181-Z90R181-Z91 and R181-Z92, and the 2U form factor R281-Z91 and R281-Z92.

These five systems all support dual AMD EPYC 7000 series processors with 32 DIMM slots. Storage wise, the R181-Z90 supports 4 x 3.5" hot-swap SATA/SAS drives, the R181-Z91 supports a mix of 2 x 2.5" hot-swap NVMe U.2 drives and 8 x hot-swap 2.5” SATA/SAS drives, while the R181-Z92 supports 10 x 2.5" hot-swap NVMe U.2 drives.

The R281-Z91 supports a mix of 6 x 2.5" hot-swap NVMe U.2 and 20 x 2.5" hot-swap SATA/SAS drives, and the R281-Z92 supports 24 x 2.5" hot-swap NVMe U.2 + 2 x 2.5” hot-swap SATA/SAS drives. These five systems also all feature 1 x NVMe M.2 slot for an additional ultra-fast storage device.

Systems that support denser, high-bandwidth storage is suited to applications such as big data, where memory size and performance to store these large volumes of data during processing is becoming increasingly important.


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