Tensor Product Attention Is All You Need. https://arxiv.org/abs/2501.06425
Mistral 7B. https://arxiv.org/abs/2310.06825
Layer-Condensed KV Cache for Efficient Inference of Large Language Models (LCKV). https://arxiv.org/abs/2405.10637
Reducing Transformer Key-Value Cache Size with Cross-Layer Attention (CLA). https://arxiv.org/abs/2405.12981
You Only Cache Once: Decoder-Decoder Architectures for Language Models (YOCO). https://arxiv.org/abs/2405.05254
H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models. https://arxiv.org/abs/2306.14048
SnapKV: LLM Knows What You are Looking for Before Generation. https://arxiv.org/abs/2404.14469
HashEvict: A Pre-Attention KV Cache Eviction Strategy using Locality-Sensitive Hashing. https://arxiv.org/abs/2412.16187
PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling. https://arxiv.org/abs/2406.02069
Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference. https://arxiv.org/abs/2407.11550
CaM: Cache Merging for Memory-Efficient LLMs Inference. https://openreview.net/forum?id=LCTmppB165
D2O: Dynamic Discriminative Operations for Efficient Generative Inference of Large Language Models. https://arxiv.org/abs/2406.13035
KVMerger: Efficiently Serving Long-Context Large Language Model by KV Cache Merging. https://arxiv.org/abs/2407.08454
KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache. https://arxiv.org/abs/2402.02750
Optimizing Inference for Long Context and Large Batch Sizes with NVFP4 KV Cache (NVIDIA). https://developer.nvidia.com/blog/optimizing-inference-for-long-context-and-large-batch-sizes-with-nvfp4-kv-cache
PALU: Compressing KV-Cache with Low-Rank Projection. https://arxiv.org/abs/2407.21118
Efficient Streaming Language Models with Attention Sinks (StreamingLLM). https://arxiv.org/abs/2309.17453
Orca: A Distributed Serving System for Transformer-Based Generative Models. https://www.usenix.org/conference/osdi22/presentation/yu
DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving. https://arxiv.org/abs/2401.09670
Splitwise: Efficient Generative LLM Inference Using Phase Splitting. https://arxiv.org/abs/2311.18677
Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving. https://arxiv.org/abs/2407.00079
Disaggregated Inference: 18 Months Later (Hao AI Lab). https://hao-ai-lab.github.io/blogs/distserve-retro/
SGLang: Efficient Execution of Structured Language Model Programs. https://arxiv.org/abs/2312.07104
LMCache: Turboboosting vLLM with 10x Faster Access to 100x More KV Caches. https://lmcache.ai/tech_report.pdf
CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving. https://arxiv.org/abs/2310.07240
CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion. https://arxiv.org/abs/2405.16444
FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU. https://arxiv.org/abs/2303.06865
InfiniGen: Efficient Generative Inference of Large Language Models with Dynamic KV Cache Management. https://www.usenix.org/conference/osdi24/presentation/lee
Introducing NVIDIA Dynamo: A Low-Latency Distributed Inference Framework. https://developer.nvidia.com/blog/introducing-nvidia-dynamo-a-low-latency-distributed-inference-framework-for-scaling-reasoning-ai-models/
MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding. https://arxiv.org/abs/2408.11049
KV Cache Compression for Long-Context Reasoning Models. https://arxiv.org/abs/2512.12008
Prompt Leakage Effect and Defense for KV-Cache Sharing in Multi-Turn Conversations (NDSS 2025). https://www.ndss-symposium.org/ndss-paper/prompt-leakage-effect-and-defense-for-kv-cache-sharing/