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SemKV: Semantic Mixed-Precision KV Cache Quantization Guided by the Quality Cliff for Long-Context LLM Inference
AI InsightThis study reveals a quality cliff in uniform KV quantization at 2.0 bits: indistinguishable from FP16 down to 2.322 bits, but collapsing at 2.0 bits. Unlike prior mixed-precision approaches chasing lower bit widths, it shows mixing only yields statistical advantage near the cliff, shifting design priorities.Key TakeawayUniform KV quantization has a quality cliff at 2.0 bits, and mixed precision only helps near the cliff.Why It MattersIt provides a concrete precision floor for long-context KV cache quantization, preventing quality collapse from aggressive compression and affecting inference cost and deployment strategy.Who's Affected- AI ResearchersCan redesign quantization evaluation based on the quality cliff and adopt multi-seed statistical protocols.
- DevelopersCan refer to the quality cliff to choose safe KV quantization bit widths when deploying long-context LLMs.
- Inference Engine DevelopersNeed to locate the cliff in quantization schedules, otherwise mixed-precision gains are marginal.
What's NextWatch whether the quality cliff holds across larger models and more quantization methods, and SemKV's end-to-end gains on real inference systems.Importance 68/100