Awesome-LLM-KV-Cache: A curated list of 📙Awesome LLM KV Cache Papers with Codes.
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Updated
Jun 17, 2026
Awesome-LLM-KV-Cache: A curated list of 📙Awesome LLM KV Cache Papers with Codes.
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[ICLR 2025] Palu: Compressing KV-Cache with Low-Rank Projection
SNDR Core Engine (Genesis) — vLLM runtime patch-overlay for Qwen3.6 + Gemma4 on consumer NVIDIA (Ampere sm_86, 2× A5000/3090). Qwen3.6-35B-A3B FP8 ~240 tok/s, 27B-int4 hybrid GDN+Mamba, Gemma4 26B/31B AWQ, 256K ctx. 321 patches: TurboQuant k8v4 KV, MTP/DFlash spec-decode, FULL cudagraph, hybrid GDN. vLLM pin dev424 + Control Center GUI.
Empirical study of KV-cache quantization in self-forcing video generation
W4A4 and INT8 KV-cache quantization for Infinity VAR models. Optimized for high-fidelity generative AI deployment on edge GPUs (e.g. NVIDIA Jetson).
Measure MLX quantization quality loss — KL divergence, perplexity, top-token agreement for KV cache and weights
Evaluation harness and norm-direction method for KV cache compression. Cross-model worst-case quality metrics.
Reproduction of TurboQuant.
MLX-native port of KVarN — variance-normalized KV-cache quantization for Apple Silicon. 3.3× compression at 71% FP16 speed, matches FP16 on GSM8K within ~2%.
Attention-aware KV cache quantization for LLM inference
Deploy Nemotron 3 Nano 30B with 1M context window on NVIDIA DGX Spark using llama.cpp (Blackwell sm_121, Q4_0 KV cache quantization)
Neutral tensor-level KV-cache quantization benchmark: 11 methods as first-class peers, per-metric leaderboards, matched-budget Pareto, multi-seed CI, and per-method fidelity tags.
KV Cache is one of the most critical optimization techniques in modern Large Language Models. However, it also creates one of the biggest memory bottlenecks in AI inference.
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