SourceReddit r/LocalLLaMA•Stalecollected in 60m
74% RAM cut for SmolLM2 on Galaxy Watch
#edge-ai#memory-optimization#wearables#androidllama.cppsmol(lm2-360mllama.cppsamsung-galaxy-watch-4ggml
💡Run 360M LLM on 380MB watch RAM – game-changer for edge inference
⚡ 30-Second TL;DR
What Changed
74% RAM reduction: 524MB to 142MB on 380MB device
Why It Matters
Enables ultra-low-resource LLM inference on wearables, expanding edge AI applications. Potential upstream PR to main llama.cpp repo could benefit broader embedded deployments.
What To Do Next
Clone the axon-dev branch from Perinban/llama.cpp and test on low-RAM Android devices.
Who should care:Developers & AI Engineers
Key Points
- •74% RAM reduction: 524MB to 142MB on 380MB device
- •Boot time improved: first 19s to 11s, second ~2.5s
- •Fix via host_ptr and mmap for CPU tensors; Vulkan tensors copied
- •Code at github.com/Perinban/llama.cpp tree axon-dev
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Original source: Reddit r/LocalLLaMA ↗
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