add SnapKV

README.md

@@ -159,7 +159,8 @@ Awesome-LLM-Inference: A curated list of [📙Awesome LLM Inference Papers with
 |2023.05|🔥🔥[**GQA**] GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints(@Google) | [[pdf]](https://arxiv.org/pdf/2305.13245.pdf)|[[flaxformer]](https://github.com/google/flaxformer) ![](https://img.shields.io/github/stars/google/flaxformer.svg?style=social) |⭐️⭐️ |  
 |2023.05|[KV Cache Compress] Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time(@)|[[pdf]](https://arxiv.org/pdf/2305.17118.pdf)|⚠️|⭐️⭐️ |  
 |2023.06|[H2O] H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models(@Rice University etc)|[[pdf]](https://arxiv.org/pdf/2306.14048.pdf)|[[H2O]](https://github.com/FMInference/H2O) ![](https://img.shields.io/github/stars/FMInference/H2O.svg?style=social) |⭐️ |  
-|2023.06|[QK-Sparse/Dropping Attention] Faster Causal Attention Over Large Sequences Through Sparse Flash Attention(@EPFL etc) |[[pdf]](https://arxiv.org/pdf/2306.01160.pdf) | [[dynamic-sparse-flash-attention]](https://github.com/epfml/dynamic-sparse-flash-attention) ![](https://img.shields.io/github/stars/epfml/dynamic-sparse-flash-attention.svg?style=social)|⭐️ |  
+|2023.06|[QK-Sparse/Dropping Attention] Faster Causal Attention Over Large Sequences Through Sparse Flash Attention(@EPFL etc) |[[pdf]](https://arxiv.org/pdf/2306.01160.pdf) | [[dynamic-sparse-flash-attention]](https://github.com/epfml/dynamic-sparse-flash-attention) ![](https://img.shields.io/github/stars/epfml/dynamic-sparse-flash-attention.svg?style=social)|⭐️ | 
+|2023.08|🔥🔥[Chunked Prefills] SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills(@Microsoft etc) | [[pdf]](https://arxiv.org/pdf/2308.16369.pdf)|⚠️|⭐️⭐️ |  
 |2023.09|🔥🔥[**PagedAttention**] Efficient Memory Management for Large Language  Model Serving with PagedAttention(@UC Berkeley etc) |[[pdf]](https://arxiv.org/pdf/2309.06180.pdf)|[[vllm]](https://github.com/vllm-project/vllm) ![](https://img.shields.io/github/stars/vllm-project/vllm.svg?style=social)|⭐️⭐️ |     
 |2023.09|[KV Cache FP8 + WINT4] Exploration on LLM inference performance optimization(@HPC4AI) | [[blog]](https://zhuanlan.zhihu.com/p/653735572)|⚠️|⭐️ |    
 |2023.10|🔥[**TensorRT-LLM KV Cache FP8**] NVIDIA TensorRT LLM(@NVIDIA) |[[docs]](https://nvidia.github.io/TensorRT-LLM/precision.html)|[[TensorRT-LLM]](https://github.com/NVIDIA/TensorRT-LLM) ![](https://img.shields.io/github/stars/NVIDIA/TensorRT-LLM.svg?style=social) |⭐️⭐️ |    
@@ -171,14 +172,15 @@ Awesome-LLM-Inference: A curated list of [📙Awesome LLM Inference Papers with
 |2024.02|🔥🔥[Prompt Caching] Efficient Prompt Caching via Embedding Similarity(@UC Berkeley)|[[pdf]](https://arxiv.org/pdf/2402.01173.pdf)|⚠️|⭐️⭐️ |  
 |2024.02|🔥🔥[Less] Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference(@CMU etc)|[[pdf]](https://arxiv.org/pdf/2402.09398.pdf)|⚠️|⭐️ |  
 |2024.02|🔥🔥[MiKV] No Token Left Behind: Reliable KV Cache Compression via Importance-Aware Mixed Precision Quantization(@KAIST)|[[pdf]](https://arxiv.org/pdf/2402.18096.pdf)|⚠️|⭐️ |  
+|2024.02|🔥🔥[Shared Prefixes] Hydragen: High-Throughput LLM Inference with Shared Prefixes | [[pdf]](https://arxiv.org/pdf/2402.05099.pdf)|⚠️|⭐️⭐️ | 
 |2024.03|🔥[QAQ] QAQ: Quality Adaptive Quantization for LLM KV Cache(@@smail.nju.edu.cn)|[[pdf]](https://arxiv.org/pdf/2403.04643.pdf)|[[QAQ-KVCacheQuantization]](https://github.com/ClubieDong/QAQ-KVCacheQuantization) ![](https://img.shields.io/github/stars/ClubieDong/QAQ-KVCacheQuantization.svg?style=social) |⭐️⭐️ |   
 |2024.03|🔥🔥[DMC] Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference(@NVIDIA etc)|[[pdf]](https://arxiv.org/pdf/2403.09636.pdf)|⚠️|⭐️⭐️ |  
 |2024.03|🔥🔥[Keyformer] Keyformer: KV Cache reduction through key tokens selection for Efficient Generative Inference(@ece.ubc.ca etc)|[[pdf]](https://arxiv.org/pdf/2403.09054.pdf)|⚠️|⭐️⭐️ | 
 |2024.03|[FASTDECODE] FASTDECODE: High-Throughput GPU-Efficient LLM Serving using Heterogeneous(@Tsinghua University)|[[pdf]](https://arxiv.org/pdf/2403.11421.pdf)|⚠️|⭐️⭐️ | 
 |2024.03|[Sparsity-Aware KV Caching] ALISA: Accelerating Large Language Model Inference via Sparsity-Aware KV Caching(@ucf.edu)|[[pdf]](https://arxiv.org/pdf/2403.17312.pdf)|⚠️|⭐️⭐️ | 
 |2024.04|[SqueezeAttention] SQUEEZEATTENTION: 2D Management of KV-Cache in LLM Inference via Layer-wise Optimal Budget(@lzu.edu.cn etc)|[[pdf]](https://arxiv.org/pdf/2404.04793.pdf)|[[SqueezeAttention]](https://github.com/hetailang/SqueezeAttention) ![](https://img.shields.io/github/stars/hetailang/SqueezeAttention.svg?style=social) |⭐️⭐️ |   
-|2024.02|🔥🔥[Shared Prefixes] Hydragen: High-Throughput LLM Inference with Shared Prefixes | [[pdf]](https://arxiv.org/pdf/2402.05099.pdf)|⚠️|⭐️⭐️ | 
-|2023.08|🔥🔥[Chunked Prefills] SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills(@Microsoft etc) | [[pdf]](https://arxiv.org/pdf/2308.16369.pdf)|⚠️|⭐️⭐️ | 
+|2024.04|[SnapKV] SnapKV: LLM Knows What You are Looking for Before Generation(@UIUC)|[[pdf]](https://arxiv.org/pdf/2404.14469)|[[SnapKV]](https://github.com/FasterDecoding/SnapKV) ![](https://img.shields.io/github/stars/FasterDecoding/SnapKV.svg?style=social)|⭐️ | 
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 ### 📖Prompt/Context Compression ([©️back👆🏻](#paperlist))    
 <div id="Context-Compression"></div>