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.
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.
According to CES 2026 material there were over 4100 exhibitors registered to attend along with several unknowns looking to take advantage of CES by hosting their own events in area hotels. It is not practical to see everything but with a good pair of shoes and a crisp Pepsi I attempted to do the impossible.
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