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Hands on with new AMD "NUC" platform from ASRock 

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It has been rumored for some time now that AMD would be re-entering the embedded/small form factor (SFF) market and with their latest generation of Ryzen CPUs, timing could not be more perfect. With the rise of Edge and IoT workloads, new constraints like cost, footprint, low to no tech-touch and power consumption is now top of mind more than ever for customers.

At VMware, we certainly see interests from customers across this spectrum whether it is a traditional "ROBO" environment running a handful of Virtual Machines for a supermarket or retail chain to an industrial site such as an oil rig or factory to mobile infrastructure like a car, ship or even a submarine. On top of these use cases, personal and development labs continue to be extremely popular amongst our customer base for learning and training purposes.

Today, the Intel NUC is still by far the most popular platform due to its size, portability, cost and low power consumption for running VMware vSphere, vSAN & NSX. The idea of an AMD "NUC" kit which includes a chassis, motherboard and CPU is something that that many folks have been asking for. With the market mostly dominated by Intel, I certainly welcome AMD re-entering this market as this will certainly drive new solutions and innovations in this market. With that said, AMD has re-entered into this market and this is initially through partnerships with several OEMs/manufacturers.


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64GB memory on the Intel NUCs?

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I just got my hands on a pair of 32GB SODIMM memory modules (64GB total) which I had been waiting to evaluate since last Fall. Apparently, it has taken some time for these high capacity memory modules to be readily available in the consumer market. Even after the announcement of the new 2018 Apple Mac Mini last year, which officially supports 32GB SODIMMS, I was not aware of any vendors who were selling these modules direct to consumers.


My primary interests in these memory modules was whether they would work on the latest Intel NUCs, specifically the Hades Canyon (NUC8i7HNK) which are the prosumer versions of the standard Intel NUCs that many folks use for vSphere Home Labs. Both the standard and Skull/Hades Canyon NUCs all officially support a maximum of 32GB of memory (2x16GB SODIMM), however it been hypothesized by the community that they *should* in theory be able to go up to 64GB, especially as some of the newer CPUs technically state support for it.

UPDATE (10/30/20) - Thanks to Ariel Sanchez who shared the Crucial 2x32GB SO-DIMM also work with the Intel NUC. It was a killer deal during Amazon Prime week, at $164 for 2x32 (64GB) but as of right now, they are going for $219 which is still cheaper than the Samsung which are going for $120 per 32GB SO-DIMM.

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Update on running ESXi on Intel NUC Hades Canyon (NUC8i7HNK & NUC8i7HVK)

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The Intel NUC is one of the most popular and affordable hardware platform for running vSphere and vSAN Home Labs. For customers that want a bit more computing power, Intel also has their Skull Canyon platform which was released back in 2016 and has also gained in popularity amongst VMware Home Labbers. To be clear, the none of the Intel NUC platforms are on VMware HCL and therefore are not officially supported.

Earlier this year, Intel released their second generation of their higher-end Intel NUCs dubbed Hades Canyon which comes in two flavors NUC8i7HNK and NUC8i7HVK, with the latter being the higher-end unit. Based on the previous generation of hardware, most customers assumed ESXi should just work and went out and purchased the lower-end "HNK" version just to find out that was not case. The ESXi Installer would boot up to a certain point and then stop with the following error:

“Shutting down firmware services…..

Using “simple offset” UEFI RTS mapping policy”

To add to the confusion, this issue was not observed with the higher-end NUC8i7HVK model which was also quite interesting. Over on the nucblog.net, they also confirmed ESXi runs fine on "HVK" model and the issue seems to be isolated to the lower-end "HNK" model.

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