A user details how they successfully run a 465GB LLM, DeepSeek V4-Pro, on a Mac Studio M3 Ultra with 512GB of unified memory. The setup prioritizes cost-effectiveness over raw speed, utilizing Apple Silicon's unified memory architecture and a tuned tooling stack including llama.cpp. Key considerations involve maximizing GPU memory allocation, managing KV cache, preventing disk swapping, and implementing robust download and server management strategies for large models. AI
IMPACT Demonstrates cost-effective local LLM inference on consumer hardware, potentially lowering barriers for developers.
RANK_REASON User-generated guide on running large models on consumer hardware.
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