VRAM optimization for gemma 4

TLDR: add -np 1 to your llama.cpp launch command if you are the only user, cuts SWA cache VRAM by 3x instantly So I was messing around with Gemma 4 and noticed the dense model hogs a massive chunk of VRAM before you even start generating anything. If you are on 16GB you might be hitting OOM and wondering why. The culprit is the SWA (Sliding Window Attention) KV cache. It allocates in F16 and does not get quantized like the rest of the KV cache. A couple days ago ggerganov merged a PR that accidentally made this worse by keeping the SWA portion unquantized even when you have KV cache quantization enabled. It got reverted about 2 hours later here https://github.com/ggml-org/llama.cpp/pull/21332 so make sure you are on a recent build. A few things that actually help with VRAM: The SWA cache s