Build local AI systems around model requirements using VRAM, system memory, GPU support, storage, thermals, quantization, and realistic upgrade paths.
Guides
Compare the hardware, specifications, and tradeoffs that matter for this buying area.
If you're shopping for hardware to run local language models, vision models, or embedding pipelines, the first mistake is usually the same one: picking a…
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You installed a local LLM tool, loaded a model, and started typing. The response comes back slowly—sometimes painfully slowly. Task Manager or Activity…
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The most common local-AI buying mistake is treating parameter count as a VRAM requirement. A 70B model does not need 70GB of VRAM—NVIDIA's own deployment…
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You downloaded a model, loaded it into your local LLM runtime, and got one of three results: an out-of-memory error, a model that loads but crawls, or a…
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You're not actually choosing between two kinds of hardware. You're choosing which bottleneck you're willing to live with: usable memory capacity or raw…
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A familiar local-AI story: someone builds a machine with 128GB of system RAM, expecting it to run a 70B-parameter model at useful speed. The model loads.…
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Most local-AI builders start by sizing storage from a single model file. A 7B parameter model in Q4 quantization is roughly 4–5 GB, so a 1 TB drive feels…
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The most common mistake in local-AI hardware planning is assuming a second GPU doubles what you have. It does not automatically double usable VRAM, and it…
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You already have a capable desktop or laptop. Maybe it runs your code, your games, or your daily work. Now you're wondering whether local AI deserves its…
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