Running AI Locally, Part 2: From VMware Context to Hands
This article explores the practical implementation of running large language models (LLMs) on local hardware. It discusses hardware requirements, benchmarking tools, and the performance limitations of various devices ranging from Raspberry Pis to high-end workstations.
Why it matters
As AI adoption grows, understanding the hardware constraints for local inference is critical for developers and enterprises looking to balance performance with data privacy.
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The article is a technical how-to guide focused on hardware performance without political or social commentary.
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