FutureMLS-Lab/OSCAR

TurboQuant: Near-optimal KV cache quantization for LLM inference (3-bit keys, 2-bit values) with Triton kernels + vLLM integration

Unified KV Cache Compression Methods for Auto-Regressive Models

KVarN is a native vLLM KV-cache quantization backend for your agents: 3-5x more context, throughput above FP16, and FP16-level accuracy. Calibration-free, one flag.

[Neurips 2025] R-KV: Redundancy-aware KV Cache Compression for Reasoning Models

LLM KV cache compression made easy

KVarN, KV cache precision tail, low-bit quants in llama.cpp for longer context of better precision in the same VRAM

LMCache: Supercharge Your LLM with the Fastest KV Cache Layer

High Performance KV Cache for LLM Inference