Local LLM VRAM Budgets: 8, 12, 16, 24 & 32 GB GPUs

Running an open-weight LLM locally? The only question that matters is what fits in your VRAM. This video breaks VRAM into a three-way budget: model weights, KV cache, and runtime overhead, using llama.cpp, GGUF quantization, and Qwen 3.8. You get concrete balanced starting points for 8, 12, 16, 24, and 32 GB GPUs, plus a simple method for sizing any model to any card: pick the intelligence you need, choose quantization, budget context and KV cache, and leave runtime headroom.

#LocalLLM #VRAM #Qwen3 #llamacpp #GGUF #OpenWeights #GPU #AI

00:00 What will actually fit on your GPU?
00:20 Weights plus KV cache plus runtime
00:38 GGUF and quantization
01:23 The KV cache
01:56 Do not over-allocate context
02:30 Runtime overhead and breathing room
02:52 It runs versus it fits
03:23 8 GB: 9B Q4 32K
04:00 12 GB: 9B Q6 64K
04:13 16 GB: 27B IQ3 32K
04:53 24 GB: 27B Q5 64K
05:27 32 GB: Q6, not Q8
06:13 Think of VRAM as a budget
06:45 Five starting points
07:09 Chat lean, agents wide