Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't
The article explains the technical advantage of unified memory architectures in mini PCs, which allow them to run large language models that exceed the VRAM capacity of high-end consumer GPUs. It highlights the trade-off between the large memory capacity of these systems and their slower processing speeds.
Why it matters
As AI models grow in size, understanding hardware limitations and alternative computing architectures is essential for developers and enthusiasts working with local LLMs.
One idea explains the whole mini PC category: unified memory lets a $2,000 box hold a 70B model no consumer GPU can fit, then decode it slowly. The roofline math, the prompt-processing catch, the NPU red herring, and the owner-measured speeds.
The content is purely technical and analytical, focusing on hardware specifications and performance benchmarks without political or social bias.
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