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Commit 2f9de7ed authored by Lukáš Krupčík's avatar Lukáš Krupčík
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4 merge requests!368Update prace.md to document the change from qprace to qprod as the default...,!367Update prace.md to document the change from qprace to qprod as the default...,!366Update prace.md to document the change from qprace to qprod as the default...,!323extended-acls-storage-section
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The [DGX-2][a] introduces NVIDIA’s new NVSwitch, enabling 300 GB/s chip-to-chip communication at 12 times the speed of PCIe.
With NVLink2, it enables sixteen Nvidia V100-SXM3 GPUs in a single system, for a total bandwidth going beyond 14 TB/s.
With NVLink2, it enables sixteen Nvidia V100-SXM3 GPUs in a single system, for a total bandwidth going beyond 14 TB/s.
Featuring pair of Xeon 8168 CPUs, 1.5 TB of memory, and 30 TB of NVMe storage,
we get a system that consumes 10 kW, weighs 163.29 kg, but offers perfomance in excess of 130TF.
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AlexNET, the network that 'started' the latest machine learning revolution, now takes 18 minutes
The topology of the DGX-2 means that all 16 GPUs are able to pool their memory into a unified memory space,
The topology of the DGX-2 means that all 16 GPUs are able to pool their memory into a unified memory space,
though with the usual tradeoffs involved if going off-chip.
The DGX-2 is able to complete the training process
The DGX-2 is able to complete the training process
for FAIRSEQ – a neural network model for language translation – 10x faster than a DGX-1 system,
bringing it down to less than two days total rather than 15.
![](../img/dgx3.png)
The DGX-2 is designed to be a powerful server in its own right.
On the storage side the DGX-2 comes with 30TB of NVMe-based solid state storage.
The DGX-2 is designed to be a powerful server in its own right.
On the storage side the DGX-2 comes with 30TB of NVMe-based solid state storage.
For clustering or further inter-system communications, it also offers InfiniBand and 100GigE connectivity, up to eight of them.
![](../img/dgx2-nvlink.png){ width=50% }
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