⚡️ Built for agentic and long-context workloads. 💡 KVarN delivers 3-5x more KV-cache capacity and up to ~1.3x the throughput of FP16, so you fit far longer contexts and serve more concurrent requests, with FP16-level accuracy . 🔌 Calibration-free, plug-and-play with vLLM. A native vLLM attention backend: add one flag, no model changes, no calibration. 🥊 Up to ~2.4× TurboQuant throughput , same capacity, higher accuracy . Why KVarN (Variance Normalized KV-Cache)? kvarn /kvɑːɳ/  ·  noun (Swedish) A grinding apparatus used to reduce substances into smaller particles or powder, especially grains, seeds, spices, coffee beans, KV-caches. KV-cache quantization usually comes with a catch. As the vLLM TurboQuant blog shows, existing methods buy extra KV-cache capacity but give up throughput (TurboQuant reports 40 to 52% lower throughput for 2.3-3.7x capacity), and aggressive low-bit quantization also tends to cost accuracy .…