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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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GPU Passthrough of Radeon RX Vega M in Intel Hades Canyon

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With the latest Intel Hades Canyon now being able to run ESXi, a number of folks have been interested in taking advantage of the integrated GPU that is included in the system. There are two models of the Hades Canyon, NUC8i7HNK which is the lower end system with Radeon RX Vega M and the NUC8i7HVK which is the higher end system with Radeon RX Vega GH. One of the first thing I had attempted after getting ESXi working on the Hades Canyon was to try to enable passthrough of the iGPU into a Windows GuestOS but in all my attempts, it resulted into a PSOD'ing the ESXi host once you start installing the AMD Drivers from Intel.

A few days ago, one of my readers, Chris78 shared an update where he was able to prevent the ESXi host from PSOD'ing by adding a VM Advanced Setting but he he was still having issues where the Windows GuestOS would now BSOD. This sounded promising, I figure it would not hurt to gave it a try and to my surprise, I was able to successfully passthrough the iGPU to a Windows 10, Windows Server 2016 and 2019 system from my limited testing. After reporting the success back to Chris78 who was still having issues even after using the settings I had used, his conclusion was there may be a difference between the HNK and HVK models, with the latter having BSOD issues. For now, it seems like iGPU can only be passthrough if you have the NUC8i7HNK model.

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MS-A2 VCF 9.0 Lab: Deploying Model Endpoint with DirectPath I/O using VMware for Private AI Services (PAIS)

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In this final blog post, we will now deploy several AI model endpoints (downloaded from Hugging Face), configure our private data source which can be a shared location (Google Drive, Confluence, Microsoft Sharepoint, or S3-compatible endpoint) or using local files and then consuming them using an AI Agent that is built using VMware for Private AI Services (PAIS).

As mentioned in the very first blog post of this mini-series, my goal was to get hands experience with PAIS but without the need to have an NVIDIA GPU capable of vGPU, which would also require an NVIDIA AI for Enterprise (NVAIE) license.

Luckily, we can use an NVIDIA GPU via DirectPath I/O, thanks to the backend plumbing the PAIS Engineering team have built and had shared with me 😊

For my proof of concept, I am using an ASUS NUC 14 Performance, which has an NVIDIA GeForce RTX 4070 mobile GPU (8GB VRAM). The ASUS NUC 14 is running alongside my Minisforum MS-A2 setup, is only used to deploy the completions model endpoint. The use of the ASUS NUC 14 is purely for prototyping and experimentation purposes to demonstrate that anyone can play with PAIS within their lab environment. I plan to use a more powerful NVIDIA GPU setup, which I will share more details at a later point for those interested.

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