Inspur NF5468M5 Power Consumption
Our Inspur NF5468M5 test server used a quadruple power supply configuration, and we wanted to measure how it performed using the Intel Xeon Gold 6130 CPUs provided in the configuration we were sent.
- Idle: 0.71kW
- STH 70% Load: 2.5kW
- 100% Load: 2.8kW
- Maximum Recorded: 3.2kW
We wanted to note here that even with a more robust configuration than our 8x Tesla P100 GPU based-Gigabyte G481-S80 8x NVIDIA Tesla GPU Server, we saw lower power consumption across the board with the Inspur Systems NF5468M5 and with more performance. For deployments with high operational costs, the Inspur NF5468M5 delivers better performance per watt.
Note these results were taken using a 208V Schneider Electric / APC PDU at 17.7C and 72% RH. Our testing window shown here had a +/- 0.3C and +/- 2% RH variance.
STH Server Spider: Inspur NF5468M5
In the second half of 2018, we introduced the STH Server Spider as a quick reference to where a server system’s aptitude lies. Our goal is to start giving a quick visual depiction of the types of parameters that a server is targeted at.
As you can see, the Inspur Systems NF5468M5 is designed for GPU compute. We also wanted to note here that unlike some of the other offerings in the market, the Inspur NF5468M5 has a fairly large number of 3.5″ bays for a GPU chassis. We have heard companies that are looking for more local storage for video often prefer having 3.5″ bays in-chassis. That is something that the Inspur solution addresses.
In this review, we have covered many aspects of the Inspur Systems NF5468M5. We gave an in-depth hardware overview. We showed the system topology as well as the web management interface. We hit various aspects of performance including CPU, GPU, networking, storage, and power consumption.
Inspur Systems has a reputation for being the largest deep learning systems provider in China. A big part of this is supplying large CSPs. The Inspur Systems NF5468M5 is designed primarily to address the needs of large CSP customers and those looking to build large training and inferencing clusters and is tailored for that purpose.