Running SmolLM2‑360M on a Samsung Galaxy Watch 4 (380MB RAM) – 74% RAM reduction in llama.cpp
I’ve got SmolLM2‑360M running on a Samsung Galaxy Watch 4 Classic (about 380MB free RAM) by tweaking llama.cpp and the underlying ggml memory model. By default, the model was being loaded twice in RAM: once via the APK’s mmap page cache and again via ggml’s tensor allocations, peaking at 524MB for a 270MB model. The fix: I pass host_ptr into llama_model_params , so CPU tensors point directly into the mmap region and only Vulkan tensors are copied. On real hardware this gives: Peak RAM: 524MB → 142MB (74% reduction) First boot: 19s → 11s Second boot: ~2.5s (mmap + KV cache warm) Code: https://github.com/Perinban/llama.cpp/tree/axon‑dev Longer write‑up with VmRSS traces and design notes: https://www.linkedin.com/posts/perinban-parameshwaran_machinelearning-llm-embeddedai-activity-74453741179
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