KV Cache Quantization for On-Device LLM Inference on Android
📰 Dev.to · SoftwareDevs mvpfactory.io
Deep dive into KV cache memory management for on-device LLM inference — covering how quantizing key-value attention caches from FP16 to INT4 with group-wise scaling reduces memory footprint by 75%, implementing sliding window eviction policies that maintain coherent multi-turn context within fixed memory budgets, and the Android-specific memory mapping strategy using ashmem regions with madvise hints that prevents OOM kills during sustained generation on memory-constrained devices
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Deep dive into KV cache memory management for on-device LLM inference — covering how quantizing key-value attention caches from FP16 to INT4 with group-wise scaling reduces memory footprint by 75%, implementing sliding window eviction policies that maintain coherent multi-turn context within fixed memory budgets, and the Android-specific memory mapping strategy using ashmem regions with madvise hints that prevents OOM kills during sustained generation on memory-constrained devices
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