A data-driven adaptive policy for KV-cache bit-width selection based on token importance features reduces decoding latency by ~18% and improves accuracy over static quantization while staying near FP16 levels on SmolLM models.
Keyformer: Kv cache reduction through key tokens selection for efficient generative inference.Proceedings of Machine Learning and Systems, 6:114–127, 2024
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Don't Waste Bits! Adaptive KV-Cache Quantization for Lightweight On-Device LLMs
A data-driven adaptive policy for KV-cache bit-width selection based on token importance features reduces decoding latency by ~18% and improves accuracy over static quantization while staying near FP16 levels on SmolLM models.