What fun is having an AI workstation if you don’t dive deeply into what makes AI work. Inference is maybe 98% of AI tasks from image generation to machine learning and even basic chats and summaries. We rely on model makers to do the heavy lifting and provide models that we can use, either in the cloud or locally.
In my previous articles I discussed the balance between model quality and hardware requirements for running AI models locally. For this article I wanted to discuss software and how my AI Workstation build was designed to work with the software packages and how I can switch between them to complete certain tasks.
Building an AI workstation is rarely about buying the single fastest component on the market; it is about finding the specific "sweet spot" that fits your workload. Whether you are prioritizing VRAM capacity, PCIe lane availability, or thermal management, the right build depends entirely on how you plan to use your machine. The goal isn't just raw power, but the ability to run the tools you need efficiently.
This is the first article in a new AI Hardware series talking about the requirements for running AI Models and why paying attention to these things can help you build a better AI Workstation. If anything learning why a DGX Spark or Mac Mini work but, can also be really slow.
2026 marks the 30th year for Lexar and they started CES off with an announcement that they were going to be the global storage partner for the Argentina National Football (Soccer) Team and launched a number of AI focused storage devices.
There were two types of products discussed during Computex. The big one was AI and the insane builds required to run those workloads followed by CAMM2 memory modules. Just about every memory vendor had modules on display and just about every motherboard maker has boards to support them however, MSI was the only one sharing their vision.
When I established Ninjalane and later Hardware Asylum the goal was to feature high-end and hard to get computer hardware. Over the years this has served me well and allowed me the chance to deep dive into some of the best motherboard designs on the planet. However, that never made me rich which seems to be a trend with the products on display by Gigabyte during Computex
I remember when Cloud Computing was a popular subject at Computex there were cloud specific motherboards available from ASRock and the same was true during the Cryptomining craze. As a result it would seem out of character if ASRock was to skip AI and, you would be right.
SilverStone is hitting the Enterprise AI segment rather heavily during Computex 2024 with a number of new chassis designs to support multiple graphics cards which can be rack mounted in a data center or standalone as a private on-premise AI compute server.
Much like with CES there are a few companies that buck the trend and hold their presentations at alternative locations. Corsair was one of them opting to have a remote display showing off the latest in gaming gear, XLR audio interfaces, teleprompters, fans and keyboards.
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