📒Introduction

Awesome-LLM-Inference: A curated list of 📙Awesome LLM Inference Papers with Codes. For Awesome Diffusion Inference, please check 📖Awesome-Diffusion-Inference . For CUDA learn notes, please check 📖CUDA-Learn-Notes .

©️Citations

@misc{Awesome-LLM-Inference@2024,
  title={Awesome-LLM-Inference: A curated list of Awesome LLM Inference Papers with codes},
  url={https://github.com/DefTruth/Awesome-LLM-Inference},
  note={Open-source software available at https://github.com/DefTruth/Awesome-LLM-Inference},
  author={DefTruth, liyucheng09 etc},
  year={2024}
}

📙Awesome LLM Inference Papers with Codes

🎉Download PDFs

Awesome LLM Inference for Beginners.pdf: 500 pages, FastServe, FlashAttention 1/2, FlexGen, FP8, LLM.int8(), PagedAttention, RoPE, SmoothQuant, WINT8/4, Continuous Batching, ZeroQuant 1/2/FP, AWQ etc.

📖Contents

• 📖Trending LLM/VLM Topics🔥🔥🔥
• 📖DP/MP/PP/TP/SP/CP Parallelism🔥🔥🔥
• 📖LLM Algorithmic/Eval Survey
• 📖LLM Train/Inference Framework/Design
• 📖Weight/Activation Quantize/Compress🔥
• 📖Continuous/In-flight Batching
• 📖IO/FLOPs-Aware/Sparse Attention🔥
• 📖KV Cache Scheduling/Quantize/Dropping🔥
• 📖Prompt/Context Compression🔥
• 📖Long Context Attention/KV Cache Optimization🔥🔥
• 📖Early-Exit/Intermediate Layer Decoding
• 📖Parallel Decoding/Sampling🔥
• 📖Structured Prune/KD/Weight Sparse
• 📖Mixture-of-Experts(MoE) LLM Inference🔥
• 📖CPU/NPU/FPGA/Mobile Inference
• 📖Non Transformer Architecture🔥
• 📖GEMM/Tensor Cores/WMMA/Parallel
• 📖VLM/Position Embed/Others

📖Trending LLM/VLM Topics (©️back👆🏻)

Date	Title	Paper	Code	Recom
2024.04	🔥🔥🔥[Open-Sora] Open-Sora: Democratizing Efficient Video Production for All(@hpcaitech)	[docs]	[Open-Sora]	⭐️⭐️
2024.04	🔥🔥🔥[Open-Sora Plan] Open-Sora Plan: This project aim to reproduce Sora (Open AI T2V model)(@PKU)	[report]	[Open-Sora-Plan]	⭐️⭐️
2024.05	🔥🔥🔥[DeepSeek-V2] DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model(@DeepSeek-AI)	[pdf]	[DeepSeek-V2]	⭐️⭐️
2024.05	🔥🔥[YOCO] You Only Cache Once: Decoder-Decoder Architectures for Language Models(@Microsoft)	[pdf]	[unilm-YOCO]	⭐️⭐️
2024.06	🔥[Mooncake] Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving(@Moonshot AI)	[pdf]	[Mooncake]	⭐️⭐️
2024.07	🔥🔥[FlashAttention-3] FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision(@TriDao etc)	[pdf]	[flash-attention]	⭐️⭐️
2024.07	🔥🔥[MInference 1.0] MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention(@Microsoft)	[pdf]	[MInference 1.0]	⭐️⭐️

📖DP/MP/PP/TP/SP/CP Parallelism (©️back👆🏻)

Date	Title	Paper	Code	Recom
2019.10	🔥🔥[MP: ZeRO] DeepSpeed-ZeRO: Memory Optimizations Toward Training Trillion Parameter Models(@microsoft.com)	[pdf]	[deepspeed]	⭐️⭐️
2020.05	🔥🔥[TP: Megatron-LM] Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism(@NVIDIA)	[pdf]	[Megatron-LM]	⭐️⭐️
2022.05	🔥🔥[SP: Megatron-LM] Megatron-LM: Reducing Activation Recomputation in Large Transformer Models(@NVIDIA)	[pdf]	[Megatron-LM]	⭐️⭐️
2023.05	🔥🔥[SP: BPT] Blockwise Parallel Transformer for Large Context Models(@UC Berkeley)	[pdf]	[RingAttention]	⭐️⭐️
2023.10	🔥🔥[SP: Ring Attention] Ring Attention with Blockwise Transformers for Near-Infinite Context(@UC Berkeley)	[pdf]	[RingAttention]	⭐️⭐️
2023.11	🔥🔥[SP: STRIPED ATTENTION] STRIPED ATTENTION: FASTER RING ATTENTION FOR CAUSAL TRANSFORMERS(@MIT etc)	[pdf]	[striped_attention]	⭐️⭐️
2023.10	🔥🔥[SP: DEEPSPEED ULYSSES] DEEPSPEED ULYSSES: SYSTEM OPTIMIZATIONS FOR ENABLING TRAINING OF EXTREME LONG SEQUENCE TRANSFORMER MODELS(@microsoft.com)	[pdf]	[deepspeed]	⭐️⭐️
2024.03	🔥🔥[CP: Megatron-LM] Megatron-LM: Context parallelism overview(@NVIDIA)	[docs]	[Megatron-LM]	⭐️⭐️
2024.05	🔥🔥[SP: Unified Sequence Parallel (USP)] YunChang: A Unified Sequence Parallel (USP) Attention for Long Context LLM Model Training and Inference(@Tencent)	[pdf]	[long-context-attention]	⭐️⭐️
2024.11	🔥🔥[CP: Meta] Context Parallelism for Scalable Million-Token Inference(@Meta Platforms, Inc)	[pdf]	⚠️	⭐️⭐️
2024.11	🔥🔥[TP: Comm Compression] Communication Compression for Tensor Parallel LLM Inference(@recogni.com)	[pdf]	⚠️	⭐️⭐️

📖LLM Algorithmic/Eval Survey (©️back👆🏻)

Date	Title	Paper	Code	Recom
2023.10	[Evaluating] Evaluating Large Language Models: A Comprehensive Survey(@tju.edu.cn)	[pdf]	[Awesome-LLMs-Evaluation]	⭐️
2023.11	🔥[Runtime Performance] Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models(@hkust-gz.edu.cn)	[pdf]	⚠️	⭐️⭐️
2023.11	[ChatGPT Anniversary] ChatGPT’s One-year Anniversary: Are Open-Source Large Language Models Catching up?(@e.ntu.edu.sg)	[pdf]	⚠️	⭐️
2023.12	[Algorithmic Survey] The Efficiency Spectrum of Large Language Models: An Algorithmic Survey(@Microsoft)	[pdf]	⚠️	⭐️
2023.12	[Security and Privacy] A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly(@Drexel University)	[pdf]	⚠️	⭐️
2023.12	🔥[LLMCompass] A Hardware Evaluation Framework for Large Language Model Inference(@princeton.edu)	[pdf]	⚠️	⭐️⭐️
2023.12	🔥[Efficient LLMs] Efficient Large Language Models: A Survey(@Ohio State University etc)	[pdf]	[Efficient-LLMs-Survey]	⭐️⭐️
2023.12	[Serving Survey] Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems(@Carnegie Mellon University)	[pdf]	⚠️	⭐️⭐️
2024.01	[Understanding LLMs] Understanding LLMs: A Comprehensive Overview from Training to Inference(@Shaanxi Normal University etc)	[pdf]	⚠️	⭐️⭐️
2024.02	[LLM-Viewer] LLM Inference Unveiled: Survey and Roofline Model Insights(@Zhihang Yuan etc)	[pdf]	[LLM-Viewer]	⭐️⭐️
2024.07	[Internal Consistency & Self-Feedback] Internal Consistency and Self-Feedback in Large Language Models: A Survey	[pdf]	[ICSF-Survey]	⭐️⭐️
2024.09	[Low-bit] A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms(@Beihang etc)	[pdf]	⚠️	⭐️⭐️
2024.10	[LLM Inference] LARGE LANGUAGE MODEL INFERENCE ACCELERATION: A COMPREHENSIVE HARDWARE PERSPECTIVE(@SJTU etc)	[pdf]	⚠️	⭐️⭐️

📖LLM Train/Inference Framework/Design (©️back👆🏻)

Date	Title	Paper	Code	Recom
2020.05	🔥[Megatron-LM] Training Multi-Billion Parameter Language Models Using Model Parallelism(@NVIDIA)	[pdf]	[Megatron-LM]	⭐️⭐️
2023.03	[FlexGen] High-Throughput Generative Inference of Large Language Models with a Single GPU(@Stanford University etc)	[pdf]	[FlexGen]	⭐️
2023.05	[SpecInfer] Accelerating Generative Large Language Model Serving with Speculative Inference and Token Tree Verification(@Peking University etc)	[pdf]	[FlexFlow]	⭐️
2023.05	[FastServe] Fast Distributed Inference Serving for Large Language Models(@Peking University etc)	[pdf]	⚠️	⭐️
2023.09	🔥[vLLM] Efficient Memory Management for Large Language Model Serving with PagedAttention(@UC Berkeley etc)	[pdf]	[vllm]	⭐️⭐️
2023.09	[StreamingLLM] EFFICIENT STREAMING LANGUAGE MODELS WITH ATTENTION SINKS(@Meta AI etc)	[pdf]	[streaming-llm]	⭐️
2023.09	[Medusa] Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads(@Tianle Cai etc)	[blog]	[Medusa]	⭐️
2023.10	🔥[TensorRT-LLM] NVIDIA TensorRT LLM(@NVIDIA)	[docs]	[TensorRT-LLM]	⭐️⭐️
2023.11	🔥[DeepSpeed-FastGen 2x vLLM?] DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference(@Microsoft)	[pdf]	[deepspeed-fastgen]	⭐️⭐️
2023.12	🔥[PETALS] Distributed Inference and Fine-tuning of Large Language Models Over The Internet(@HSE Univesity etc)	[pdf]	[petals]	⭐️⭐️
2023.10	[LightSeq] LightSeq: Sequence Level Parallelism for Distributed Training of Long Context Transformers(@UC Berkeley etc)	[pdf]	[LightSeq]	⭐️
2023.12	[PowerInfer] PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU(@SJTU)	[pdf]	[PowerInfer]	⭐️
2024.01	[inferflow]INFERFLOW: AN EFFICIENT AND HIGHLY CONFIGURABLE INFERENCE ENGINE FOR LARGE LANGUAGE MODELS(@Tencent AI Lab)	[pdf]	[inferflow]	⭐️
2024.06	🔥[Mooncake] Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving(@Moonshot AI)	[pdf]	[Mooncake]	⭐️⭐️
2023.06	🔥[LMDeploy] LMDeploy: LMDeploy is a toolkit for compressing, deploying, and serving LLMs(@InternLM)	[docs]	[lmdeploy]	⭐️⭐️
2023.05	🔥[MLC-LLM]Universal LLM Deployment Engine with ML Compilation(@mlc-ai)	[docs]	[mlc-llm]	⭐️⭐️
2023.08	🔥[LightLLM] LightLLM is a Python-based LLM (Large Language Model) inference and serving framework(@ModelTC)	[docs]	[lightllm]	⭐️⭐️
2023.03	🔥[llama.cpp] llama.cpp: Inference of Meta's LLaMA model (and others) in pure C/C++(@ggerganov)	[docs]	[llama.cpp]	⭐️⭐️
2024.02	🔥[flashinfer] FlashInfer: Kernel Library for LLM Serving(@flashinfer-ai)	[docs]	[flashinfer]	⭐️⭐️
2024.06	🔥[Mooncake] Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving(@Moonshot AI)	[pdf]	[Mooncake]	⭐️⭐️
2024.07	🔥[DynamoLLM] DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency(@Microsoft Azure Research)	[pdf]	⚠️	⭐️
2024.08	🔥[NanoFlow] NanoFlow: Towards Optimal Large Language Model Serving Throughput(@University of Washington)	[pdf]	[Nanoflow]	⭐️⭐️
2024.08	🔥[Decentralized LLM] Decentralized LLM Inference over Edge Networks with Energy Harvesting(@Padova)	[pdf]	⚠️	⭐️

📖Continuous/In-flight Batching (©️back👆🏻)

Date	Title	Paper	Code	Recom
2022.07	🔥[Continuous Batching] Orca: A Distributed Serving System for Transformer-Based Generative Models(@Seoul National University etc)	[pdf]	⚠️	⭐️⭐️
2023.10	🔥[In-flight Batching] NVIDIA TensorRT LLM Batch Manager(@NVIDIA)	[docs]	[TensorRT-LLM]	⭐️⭐️
2023.11	🔥[DeepSpeed-FastGen 2x vLLM?] DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference(@Microsoft)	[blog]	[deepspeed-fastgen]	⭐️⭐️
2023.11	[Splitwise] Splitwise: Efficient Generative LLM Inference Using Phase Splitting(@Microsoft etc)	[pdf]	⚠️	⭐️
2023.12	[SpotServe] SpotServe: Serving Generative Large Language Models on Preemptible Instances(@cmu.edu etc)	[pdf]	[SpotServe]	⭐️
2023.10	[LightSeq] LightSeq: Sequence Level Parallelism for Distributed Training of Long Context Transformers(@UC Berkeley etc)	[pdf]	[LightSeq]	⭐️
2024.05	🔥[vAttention] vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention(@Microsoft Research India)	[pdf]	[vAttention]	⭐️⭐️
2024.07	🔥🔥[vTensor] vTensor: Flexible Virtual Tensor Management for Efficient LLM Serving(@Shanghai Jiao Tong University etc)	[pdf]	[vTensor]	⭐️⭐️
2024.08	🔥[Automatic Inference Engine Tuning] Towards SLO-Optimized LLM Serving via Automatic Inference Engine Tuning(@Nanjing University etc)	[pdf]	⚠️	⭐️⭐️
2024.08	🔥[SJF Scheduling] Efficient LLM Scheduling by Learning to Rank(@UCSD etc)	[pdf]	⚠️	⭐️⭐️

📖Weight/Activation Quantize/Compress (©️back👆🏻)

Date	Title	Paper	Code	Recom
2022.06	🔥[ZeroQuant] Efficient and Affordable Post-Training Quantization for Large-Scale Transformers(@Microsoft)	[pdf]	[DeepSpeed]	⭐️⭐️
2022.08	[FP8-Quantization] FP8 Quantization: The Power of the Exponent(@Qualcomm AI Research)	[pdf]	[FP8-quantization]	⭐️
2022.08	[LLM.int8()] 8-bit Matrix Multiplication for Transformers at Scale(@Facebook AI Research etc)	[pdf]	[bitsandbytes]	⭐️
2022.10	🔥[GPTQ] GPTQ: ACCURATE POST-TRAINING QUANTIZATION FOR GENERATIVE PRE-TRAINED TRANSFORMERS(@IST Austria etc)	[pdf]	[gptq]	⭐️⭐️
2022.11	🔥[WINT8/4] Who Says Elephants Can’t Run: Bringing Large Scale MoE Models into Cloud Scale Production(@NVIDIA&Microsoft)	[pdf]	[FasterTransformer]	⭐️⭐️
2022.11	🔥[SmoothQuant] Accurate and Efficient Post-Training Quantization for Large Language Models(@MIT etc)	[pdf]	[smoothquant]	⭐️⭐️
2023.03	[ZeroQuant-V2] Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation(@Microsoft)	[pdf]	[DeepSpeed]	⭐️
2023.06	🔥[AWQ] AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration(@MIT etc)	[pdf]	[llm-awq]	⭐️⭐️
2023.06	[SpQR] SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression(@University of Washington etc)	[pdf]	[SpQR]	⭐️
2023.06	[SqueezeLLM] SQUEEZELLM: DENSE-AND-SPARSE QUANTIZATION(@berkeley.edu)	[pdf]	[SqueezeLLM]	⭐️
2023.07	[ZeroQuant-FP] A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats(@Microsoft)	[pdf]	[DeepSpeed]	⭐️
2023.09	[KV Cache FP8 + WINT4] Exploration on LLM inference performance optimization(@HPC4AI)	[blog]	⚠️	⭐️
2023.10	[FP8-LM] FP8-LM: Training FP8 Large Language Models(@Microsoft etc)	[pdf]	[MS-AMP]	⭐️
2023.10	[LLM-Shearing] SHEARED LLAMA: ACCELERATING LANGUAGE MODEL PRE-TRAINING VIA STRUCTURED PRUNING(@cs.princeton.edu etc)	[pdf]	[LLM-Shearing]	⭐️
2023.10	[LLM-FP4] LLM-FP4: 4-Bit Floating-Point Quantized Transformers(@ust.hk&meta etc)	[pdf]	[LLM-FP4]	⭐️
2023.11	[2-bit LLM] Enabling Fast 2-bit LLM on GPUs: Memory Alignment, Sparse Outlier, and Asynchronous Dequantization(@Shanghai Jiao Tong University etc)	[pdf]	⚠️	⭐️
2023.12	[SmoothQuant+] SmoothQuant+: Accurate and Efficient 4-bit Post-Training Weight Quantization for LLM(@ZTE Corporation)	[pdf]	[smoothquantplus]	⭐️
2023.11	[OdysseyLLM W4A8] A Speed Odyssey for Deployable Quantization of LLMs(@meituan.com)	[pdf]	⚠️	⭐️
2023.12	🔥[SparQ] SPARQ ATTENTION: BANDWIDTH-EFFICIENT LLM INFERENCE(@graphcore.ai)	[pdf]	⚠️	⭐️⭐️
2023.12	[Agile-Quant] Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge(@Northeastern University&Oracle)	[pdf]	⚠️	⭐️
2023.12	[CBQ] CBQ: Cross-Block Quantization for Large Language Models(@ustc.edu.cn)	[pdf]	⚠️	⭐️
2023.10	[QLLM] QLLM: ACCURATE AND EFFICIENT LOW-BITWIDTH QUANTIZATION FOR LARGE LANGUAGE MODELS(@ZIP Lab&SenseTime Research etc)	[pdf]	⚠️	⭐️
2024.01	[FP6-LLM] FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design(@Microsoft etc)	[pdf]	⚠️	⭐️
2024.05	🔥🔥[W4A8KV4] QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving(@MIT&NVIDIA)	[pdf]	[qserve]	⭐️⭐️
2024.05	🔥[SpinQuant] SpinQuant: LLM Quantization with Learned Rotations(@Meta)	[pdf]	⚠️	⭐️
2024.05	🔥[I-LLM] I-LLM: Efficient Integer-Only Inference for Fully-Quantized Low-Bit Large Language Models(@Houmo AI)	[pdf]	⚠️	⭐️
2024.06	🔥[OutlierTune] OutlierTune: Efficient Channel-Wise Quantization for Large Language Models(@Beijing University)	[pdf]	⚠️	⭐️
2024.06	🔥[GPTQT] GPTQT: Quantize Large Language Models Twice to Push the Efficiency(@zju)	[pdf]	⚠️	⭐️
2024.08	🔥[ABQ-LLM] ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models(@ByteDance)	[pdf]	[ABQ-LLM]	⭐️
2024.08	🔥[1-bit LLMs] Matmul or No Matmal in the Era of 1-bit LLMs(@University of South Carolina)	[pdf]	⚠️	⭐️
2024.08	🔥[ACTIVATION SPARSITY] TRAINING-FREE ACTIVATION SPARSITY IN LARGE LANGUAGE MODELS(@MIT etc)	[pdf]	[TEAL]	⭐️
2024.09	🔥[VPTQ] VPTQ: EXTREME LOW-BIT VECTOR POST-TRAINING QUANTIZATION FOR LARGE LANGUAGE MODELS(@Microsoft)	[pdf]	[VPTQ]	⭐️
2024.11	🔥[BitNet] BitNet a4.8: 4-bit Activations for 1-bit LLMs(@Microsoft)	[pdf]	[bitnet]	⭐️

📖IO/FLOPs-Aware/Sparse Attention (©️back👆🏻)

Date	Title	Paper	Code	Recom
2018.05	[Online Softmax] Online normalizer calculation for softmax(@NVIDIA)	[pdf]	⚠️	⭐️
2019.11	🔥[MQA] Fast Transformer Decoding: One Write-Head is All You Need(@Google)	[pdf]	⚠️	⭐️⭐️
2020.10	[Hash Attention] REFORMER: THE EFFICIENT TRANSFORMER(@Google)	[pdf]	[reformer]	⭐️⭐️
2022.05	🔥[FlashAttention] Fast and Memory-Efficient Exact Attention with IO-Awareness(@Stanford University etc)	[pdf]	[flash-attention]	⭐️⭐️
2022.10	[Online Softmax] SELF-ATTENTION DOES NOT NEED O(n^2) MEMORY(@Google)	[pdf]	⚠️	⭐️
2023.05	[FlashAttention] From Online Softmax to FlashAttention(@cs.washington.edu)	[pdf]	⚠️	⭐️⭐️
2023.05	[FLOP, I/O] Dissecting Batching Effects in GPT Inference(@Lequn Chen)	[blog]	⚠️	⭐️
2023.05	🔥🔥[GQA] GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints(@Google)	[pdf]	[flaxformer]	⭐️⭐️
2023.06	[Sparse FlashAttention] Faster Causal Attention Over Large Sequences Through Sparse Flash Attention(@EPFL etc)	[pdf]	[dynamic-sparse-flash-attention]	⭐️
2023.07	🔥[FlashAttention-2] Faster Attention with Better Parallelism and Work Partitioning(@Stanford University etc)	[pdf]	[flash-attention]	⭐️⭐️
2023.10	🔥[Flash-Decoding] Flash-Decoding for long-context inference(@Stanford University etc)	[blog]	[flash-attention]	⭐️⭐️
2023.11	[Flash-Decoding++] FLASHDECODING++: FASTER LARGE LANGUAGE MODEL INFERENCE ON GPUS(@Tsinghua University&Infinigence-AI)	[pdf]	⚠️	⭐️
2023.01	[SparseGPT] SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot(@ISTA etc)	[pdf]	[sparsegpt]	⭐️
2023.12	🔥[GLA] Gated Linear Attention Transformers with Hardware-Efficient Training(@MIT-IBM Watson AI)	[pdf]	gated_linear_attention	⭐️⭐️
2023.12	[SCCA] SCCA: Shifted Cross Chunk Attention for long contextual semantic expansion(@Beihang University)	[pdf]	⚠️	⭐️
2023.12	🔥[FlashLLM] LLM in a flash: Efficient Large Language Model Inference with Limited Memory(@Apple)	[pdf]	⚠️	⭐️⭐️
2024.03	🔥🔥[CHAI] CHAI: Clustered Head Attention for Efficient LLM Inference(@cs.wisc.edu etc)	[pdf]	⚠️	⭐️⭐️
2024.04	🔥🔥[DeFT] DeFT: Decoding with Flash Tree-Attention for Efficient Tree-structured LLM Inference(@Westlake University etc)	[pdf]	⚠️	⭐️⭐️
2024.04	[MoA] MoA: Mixture of Sparse Attention for Automatic Large Language Model Compression(@thu et el.)	[pdf]	[MoA]	⭐️
2024.07	🔥🔥[FlashAttention-3] FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision(@TriDao etc)	[pdf]	[flash-attention]	⭐️⭐️
2024.07	🔥🔥[MInference 1.0] MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention(@Microsoft)	[pdf]	[MInference 1.0]	⭐️⭐️
2024.07	🔥🔥[Shared Attention] Beyond KV Caching: Shared Attention for Efficient LLMs(@Kyushu University etc)	[pdf]	[shareAtt]	⭐️
2024.09	🔥🔥[CHESS] CHESS : Optimizing LLM Inference via Channel-Wise Thresholding and Selective Sparsification(@Wuhan University)	[pdf]	⚠️	⭐️⭐️
2024.09	🔥🔥[INT-FLASHATTENTION] INT-FLASHATTENTION: ENABLING FLASH ATTENTION FOR INT8 QUANTIZATION(@PKU etc)	[pdf]	[INT-FlashAttention]	⭐️

📖KV Cache Scheduling/Quantize/Dropping (©️back👆🏻)

Date	Title	Paper	Code	Recom
2019.11	🔥[MQA] Fast Transformer Decoding: One Write-Head is All You Need(@Google)	[pdf]	⚠️	⭐️⭐️
2022.06	[LTP] Learned Token Pruning for Transformers(@UC Berkeley etc)	[pdf]	[LTP]	⭐️
2023.05	🔥🔥[GQA] GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints(@Google)	[pdf]	[flaxformer]	⭐️⭐️
2023.05	[KV Cache Compress] Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time(@)	[pdf]	⚠️	⭐️⭐️
2023.06	[H2O] H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models(@Rice University etc)	[pdf]	[H2O]	⭐️
2023.06	[QK-Sparse/Dropping Attention] Faster Causal Attention Over Large Sequences Through Sparse Flash Attention(@EPFL etc)	[pdf]	[dynamic-sparse-flash-attention]	⭐️
2023.08	🔥🔥[Chunked Prefills] SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills(@Microsoft etc)	[pdf]	⚠️	⭐️⭐️
2023.09	🔥🔥[PagedAttention] Efficient Memory Management for Large Language Model Serving with PagedAttention(@UC Berkeley etc)	[pdf]	[vllm]	⭐️⭐️
2023.09	[KV Cache FP8 + WINT4] Exploration on LLM inference performance optimization(@HPC4AI)	[blog]	⚠️	⭐️
2023.10	🔥[TensorRT-LLM KV Cache FP8] NVIDIA TensorRT LLM(@NVIDIA)	[docs]	[TensorRT-LLM]	⭐️⭐️
2023.10	🔥[Adaptive KV Cache Compress] MODEL TELLS YOU WHAT TO DISCARD: ADAPTIVE KV CACHE COMPRESSION FOR LLMS(@illinois.edu&microsoft)	[pdf]	⚠️	⭐️⭐️
2023.10	[CacheGen] CacheGen: Fast Context Loading for Language Model Applications(@Chicago University&Microsoft)	[pdf]	[LMCache]	⭐️
2023.12	[KV-Cache Optimizations] Leveraging Speculative Sampling and KV-Cache Optimizations Together for Generative AI using OpenVINO(@Haim Barad etc)	[pdf]	⚠️	⭐️
2023.12	[KV Cache Compress with LoRA] Compressed Context Memory for Online Language Model Interaction (@SNU & NAVER AI)	[pdf]	[Compressed-Context-Memory]	⭐️⭐️
2023.12	🔥🔥[RadixAttention] Efficiently Programming Large Language Models using SGLang(@Stanford University etc)	[pdf]	[sglang]	⭐️⭐️
2024.01	🔥🔥[DistKV-LLM] Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache(@Alibaba etc)	[pdf]	⚠️	⭐️⭐️
2024.02	🔥🔥[Prompt Caching] Efficient Prompt Caching via Embedding Similarity(@UC Berkeley)	[pdf]	⚠️	⭐️⭐️
2024.02	🔥🔥[Less] Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference(@CMU etc)	[pdf]	⚠️	⭐️
2024.02	🔥🔥[MiKV] No Token Left Behind: Reliable KV Cache Compression via Importance-Aware Mixed Precision Quantization(@KAIST)	[pdf]	⚠️	⭐️
2024.02	🔥🔥[Shared Prefixes] Hydragen: High-Throughput LLM Inference with Shared Prefixes	[pdf]	⚠️	⭐️⭐️
2024.02	🔥🔥[ChunkAttention] ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition(@microsoft.com)	[pdf]	[chunk-attention]	⭐️⭐️
2024.03	🔥[QAQ] QAQ: Quality Adaptive Quantization for LLM KV Cache(@@smail.nju.edu.cn)	[pdf]	[QAQ-KVCacheQuantization]	⭐️⭐️
2024.03	🔥🔥[DMC] Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference(@NVIDIA etc)	[pdf]	⚠️	⭐️⭐️
2024.03	🔥🔥[Keyformer] Keyformer: KV Cache reduction through key tokens selection for Efficient Generative Inference(@ece.ubc.ca etc)	[pdf]	[Keyformer]	⭐️⭐️
2024.03	[FASTDECODE] FASTDECODE: High-Throughput GPU-Efficient LLM Serving using Heterogeneous(@Tsinghua University)	[pdf]	⚠️	⭐️⭐️
2024.03	[Sparsity-Aware KV Caching] ALISA: Accelerating Large Language Model Inference via Sparsity-Aware KV Caching(@ucf.edu)	[pdf]	⚠️	⭐️⭐️
2024.03	🔥[GEAR] GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM(@gatech.edu)	[pdf]	[GEAR]	⭐️
2024.04	[SqueezeAttention] SQUEEZEATTENTION: 2D Management of KV-Cache in LLM Inference via Layer-wise Optimal Budget(@lzu.edu.cn etc)	[pdf]	[SqueezeAttention]	⭐️⭐️
2024.04	[SnapKV] SnapKV: LLM Knows What You are Looking for Before Generation(@UIUC)	[pdf]	[SnapKV]	⭐️
2024.05	🔥[vAttention] vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention(@Microsoft Research India)	[pdf]	[vAttention]	⭐️⭐️
2024.05	🔥[KVCache-1Bit] KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization(@Rice University)	[pdf]	⚠️	⭐️⭐️
2024.05	🔥[KV-Runahead] KV-Runahead: Scalable Causal LLM Inference by Parallel Key-Value Cache Generation(@Apple etc)	[pdf]	⚠️	⭐️⭐️
2024.05	🔥[ZipCache] ZipCache: Accurate and Efficient KV Cache Quantization with Salient Token Identification(@Zhejiang University etc)	[pdf]	⚠️	⭐️⭐️
2024.05	🔥[MiniCache] MiniCache: KV Cache Compression in Depth Dimension for Large Language Models(@ZIP Lab)	[pdf]	⚠️	⭐️⭐️
2024.05	🔥[CacheBlend] CacheBlend: Fast Large Language Model Serving with Cached Knowledge Fusion(@University of Chicago)	[pdf]	[LMCache]	⭐️⭐️
2024.06	🔥[CompressKV] Effectively Compress KV Heads for LLM(@alibaba etc)	[pdf]	⚠️	⭐️⭐️
2024.06	🔥[MemServe] MemServe: Context Caching for Disaggregated LLM Serving with Elastic Memory Pool(@Huawei Cloud etc)	[pdf]	⚠️	⭐️⭐️
2024.07	🔥[MLKV] MLKV: Multi-Layer Key-Value Heads for Memory Efficient Transformer Decoding(@Institut Teknologi Bandung)	[pdf]	[pythia-mlkv]	⭐️
2024.07	🔥[ThinK] ThinK: Thinner Key Cache by Query-Driven Pruning(@Salesforce AI Research etc)	[pdf]	⚠️	⭐️⭐️
2024.07	🔥[Palu] Palu: Compressing KV-Cache with Low-Rank Projection(@nycu.edu.tw)	[pdf]	[Palu]	⭐️⭐️
2024.08	🔥[Zero-Delay QKV Compression] Zero-Delay QKV Compression for Mitigating KV Cache and Network Bottlenecks in LLM Inference(@University of Virginia)	[pdf]	⚠️	⭐️⭐️
2024.09	🔥[AlignedKV] AlignedKV: Reducing Memory Access of KV-Cache with Precision-Aligned Quantization(@Tsinghua University)	[pdf]	[AlignedKV]	⭐️
2024.10	🔥[LayerKV] Optimizing Large Language Model Serving with Layer-wise KV Cache Management(@Ant Group)	[pdf]	⚠️	⭐️⭐️
2024.10	🔥[AdaKV] Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference (@USTC)	[pdf]	[AdaKV]	⭐️⭐️

📖Prompt/Context/KV Compression (©️back👆🏻)

Date	Title	Paper	Code	Recom
2023.04	🔥[Selective-Context] Compressing Context to Enhance Inference Efficiency of Large Language Models(@Surrey)	[pdf]	Selective-Context	⭐️⭐️
2023.05	[AutoCompressor] Adapting Language Models to Compress Contextss(@Princeton)	[pdf]	AutoCompressor	⭐️
2023.10	🔥[LLMLingua] LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models(@Microsoft)	[pdf]	LLMLingua	⭐️⭐️
2023.10	🔥🔥[LongLLMLingua] LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression(@Microsoft)	[pdf]	LLMLingua	⭐️⭐️
2024.03	🔥[LLMLingua-2] LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression(@Microsoft)	[pdf]	LLMLingua series	⭐️
2024.08	🔥🔥[500xCompressor] 500xCompressor: Generalized Prompt Compression for Large Language Models(@University of Cambridge)	[pdf]	⚠️	⭐️⭐️
2024.08	🔥🔥[Eigen Attention] Eigen Attention: Attention in Low-Rank Space for KV Cache Compression(@purdue.edu)	[pdf]	⚠️	⭐️⭐️
2024.09	🔥🔥[Prompt Compression] Prompt Compression with Context-Aware Sentence Encoding for Fast and Improved LLM Inference(@Alterra AI)	[pdf]	⚠️	⭐️⭐️
2024.09	🔥🔥[Context Distillation] Efficient LLM Context Distillation(@gatech.edu)	[pdf]	⚠️	⭐️⭐️
2024.09	🔥🔥[CRITIPREFILL] CRITIPREFILL: A SEGMENT-WISE CRITICALITYBASED APPROACH FOR PREFILLING ACCELERATION IN LLMS(@OPPO)	[pdf]	CritiPrefill	⭐️
2024.10	🔥🔥[KV-COMPRESS] PAGED KV-CACHE COMPRESSION WITH VARIABLE COMPRESSION RATES PER ATTENTION HEAD(@Cloudflare, inc.)	[pdf]	vllm-kvcompress	⭐️⭐️
2024.10	🔥🔥[LORC] Low-Rank Compression for LLMs KV Cache with a Progressive Compression Strategy(@gatech.edu)	[pdf]	⚠️	⭐️⭐️

📖Long Context Attention/KV Cache Optimization (©️back👆🏻)

Date	Title	Paper	Code	Recom
2023.05	🔥🔥[Blockwise Attention] Blockwise Parallel Transformer for Large Context Models(@UC Berkeley)	[pdf]	⚠️	⭐️⭐️
2023.05	🔥[Landmark Attention] Random-Access Infinite Context Length for Transformers(@epfl.ch)	[pdf]	landmark-attention	⭐️⭐️
2023.07	🔥[LightningAttention-1] TRANSNORMERLLM: A FASTER AND BETTER LARGE LANGUAGE MODEL WITH IMPROVED TRANSNORMER(@OpenNLPLab)	[pdf]	TransnormerLLM	⭐️⭐️
2023.07	🔥[LightningAttention-2] Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models(@OpenNLPLab)	[pdf]	lightning-attention	⭐️⭐️
2023.10	🔥🔥[RingAttention] Ring Attention with Blockwise Transformers for Near-Infinite Context(@UC Berkeley)	[pdf]	[RingAttention]	⭐️⭐️
2023.11	🔥[HyperAttention] HyperAttention: Long-context Attention in Near-Linear Time(@yale&Google)	[pdf]	hyper-attn	⭐️⭐️
2023.11	[Streaming Attention] One Pass Streaming Algorithm for Super Long Token Attention Approximation in Sublinear Space(@Adobe Research etc)	[pdf]	⚠️	⭐️
2023.11	🔥[Prompt Cache] PROMPT CACHE: MODULAR ATTENTION REUSE FOR LOW-LATENCY INFERENCE(@Yale University etc)	[pdf]	⚠️	⭐️⭐️
2023.11	🔥🔥[StripedAttention] STRIPED ATTENTION: FASTER RING ATTENTION FOR CAUSAL TRANSFORMERS(@MIT etc)	[pdf]	[striped_attention]	⭐️⭐️
2024.01	🔥🔥[KVQuant] KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization(@UC Berkeley)	[pdf]	[KVQuant]	⭐️⭐️
2024.02	🔥[RelayAttention] RelayAttention for Efficient Large Language Model Serving with Long System Prompts(@sensetime.com etc)	[pdf]	⚠️	⭐️⭐️
2024.04	🔥🔥[Infini-attention] Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention(@Google)	[pdf]	⚠️	⭐️⭐️
2024.04	🔥🔥[RAGCache] RAGCache: Efficient Knowledge Caching for Retrieval-Augmented Generation(@Peking University&ByteDance Inc)	[pdf]	⚠️	⭐️⭐️
2024.04	🔥🔥[KCache] EFFICIENT LLM INFERENCE WITH KCACHE(@Qiaozhi He, Zhihua Wu)	[pdf]	⚠️	⭐️⭐️
2024.05	🔥🔥[YOCO] You Only Cache Once: Decoder-Decoder Architectures for Language Models(@Microsoft)	[pdf]	[unilm-YOCO]	⭐️⭐️
2024.05	🔥🔥[SKVQ] SKVQ: Sliding-window Key and Value Cache Quantization for Large Language Models(@Shanghai AI Laboratory)	[pdf]	⚠️	⭐️⭐️
2024.05	🔥🔥[CLA] Reducing Transformer Key-Value Cache Size with Cross-Layer Attention(@MIT-IBM)	[pdf]	⚠️	⭐️⭐️
2024.06	🔥[LOOK-M] LOOK-M: Look-Once Optimization in KV Cache for Efficient Multimodal Long-Context Inference(@osu.edu etc)	[pdf]	[LOOK-M]	⭐️⭐️
2024.06	🔥🔥[MInference] MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention(@Microsoft etc)	[pdf]	[MInference]	⭐️⭐️
2024.06	🔥🔥[InfiniGen] InfiniGen: Efficient Generative Inference of Large Language Models with Dynamic KV Cache Management(@snu)	[pdf]	⚠️	⭐️⭐️
2024.06	🔥🔥[Quest] Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference(@mit-han-lab etc)	[pdf]	[Quest]	⭐️⭐️
2024.07	🔥[PQCache] PQCache: Product Quantization-based KVCache for Long Context LLM Inference(@PKU etc)	[pdf]	⚠️	⭐️⭐️
2024.08	🔥[SentenceVAE] SentenceVAE: Faster, Longer and More Accurate Inference with Next-sentence Prediction for Large Language Models(@TeleAI)	[pdf]	⚠️	⭐️⭐️
2024.09	🔥[InstInfer] InstInfer: In-Storage Attention Offloading for Cost-Effective Long-Context LLM Inference(@PKU etc)	[pdf]	⚠️	⭐️⭐️
2024.09	🔥[RetrievalAttention] RetrievalAttention: Accelerating Long-Context LLM Inference via Vector Retrieval(@microsoft.com)	[pdf]	⚠️	⭐️⭐️
2024.10	🔥[ShadowKV] ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference(@CMU & bytedance)	[pdf]	[ShadowKV]	⭐️⭐️

📖Early-Exit/Intermediate Layer Decoding (©️back👆🏻)

Date	Title	Paper	Code	Recom
2020.04	[DeeBERT] DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference(@uwaterloo.ca)	[pdf]	⚠️	⭐️
2020.04	[FastBERT] FastBERT: a Self-distilling BERT with Adaptive Inference Time(@PKU)	[pdf]	[FastBERT]	⭐️
2021.06	[BERxiT] BERxiT: Early Exiting for BERT with Better Fine-Tuning and Extension to Regression(@uwaterloo.ca)	[pdf]	[berxit]	⭐️
2023.06	🔥[SkipDecode] SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference(@Microsoft)	[pdf]	⚠️	⭐️
2023.10	🔥[LITE] Accelerating LLaMA Inference by Enabling Intermediate Layer Decoding via Instruction Tuning with LITE(@Arizona State University)	[pdf]	⚠️	⭐️⭐️
2023.12	🔥🔥[EE-LLM] EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism(@alibaba-inc.com)	[pdf]	[EE-LLM]	⭐️⭐️
2023.10	🔥[FREE] Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding(@KAIST AI&AWS AI)	[pdf]	[fast_robust_early_exit]	⭐️⭐️
2024.02	🔥[EE-Tuning] EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models(@alibaba-inc.com)	[pdf]	[EE-Tuning]	⭐️⭐️
2024.07	[Skip Attention] Attention Is All You Need But You Don’t Need All Of It For Inference of Large Language Models(@University College London)	[pdf]	⚠️	⭐️⭐️
2024.08	[KOALA] KOALA: Enhancing Speculative Decoding for LLM via Multi-Layer Draft Heads with Adversarial Learning(@Dalian University)	[pdf]	⚠️	⭐️⭐️

📖Parallel Decoding/Sampling (©️back👆🏻)

Date	Title	Paper	Code	Recom
2018.11	🔥[Parallel Decoding] Blockwise Parallel Decoding for Deep Autoregressive Models(@Berkeley&Google)	[pdf]	⚠️	⭐️⭐️
2023.02	🔥[Speculative Sampling] Accelerating Large Language Model Decoding with Speculative Sampling(@DeepMind)	[pdf]	⚠️	⭐️⭐️
2023.05	🔥[Speculative Sampling] Fast Inference from Transformers via Speculative Decoding(@Google Research etc)	[pdf]	[LLMSpeculativeSampling]	⭐️⭐️
2023.09	🔥[Medusa] Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads(@Tianle Cai etc)	[pdf]	[Medusa]	⭐️⭐️
2023.10	[OSD] Online Speculative Decoding(@UC Berkeley etc)	[pdf]	⚠️	⭐️⭐️
2023.12	[Cascade Speculative] Cascade Speculative Drafting for Even Faster LLM Inference(@illinois.edu)	[pdf]	⚠️	⭐️
2024.02	🔥[LookaheadDecoding] Break the Sequential Dependency of LLM Inference Using LOOKAHEAD DECODING(@UCSD&Google&UC Berkeley)	[pdf]	[LookaheadDecoding]	⭐️⭐️
2024.02	🔥🔥[Speculative Decoding] Decoding Speculative Decoding(@cs.wisc.edu)	[pdf]	Decoding Speculative Decoding	⭐️
2024.04	🔥🔥[TriForce] TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding(@cmu.edu&Meta AI)	[pdf]	[TriForce]	⭐️⭐️
2024.04	🔥🔥[Hidden Transfer] Parallel Decoding via Hidden Transfer for Lossless Large Language Model Acceleration(@pku.edu.cn etc)	[pdf]	⚠️	⭐️
2024.05	🔥[Instructive Decoding] INSTRUCTIVE DECODING: INSTRUCTION-TUNED LARGE LANGUAGE MODELS ARE SELF-REFINER FROM NOISY INSTRUCTIONS(@KAIST AI)	[pdf]	[Instructive-Decoding]	⭐️
2024.05	🔥[S3D] S3D: A Simple and Cost-Effective Self-Speculative Decoding Scheme for Low-Memory GPUs(@lge.com)	[pdf]	⚠️	⭐️
2024.06	🔥[Parallel Decoding] Exploring and Improving Drafts in Blockwise Parallel Decoding(@KAIST&Google Research)	[pdf]	⚠️	⭐️⭐️
2024.07	🔥[Multi-Token Speculative Decoding] Multi-Token Joint Speculative Decoding for Accelerating Large Language Model Inference(@University of California, etc)	[pdf]	⚠️	⭐️⭐️
2024.08	🔥[Token Recycling] Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling(@ir.hit.edu.cn etc)	[pdf]	⚠️	⭐️⭐️
2024.08	🔥[Speculative Decoding] Parallel Speculative Decoding with Adaptive Draft Length(@USTC etc)	[pdf]	[PEARL]	⭐️⭐️
2024.08	🔥[FocusLLM] FocusLLM: Scaling LLM’s Context by Parallel Decoding(@Tsinghua University etc)	[pdf]	[FocusLLM]	⭐️
2024.08	🔥[MagicDec] MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding(@CMU etc)	[pdf]	[MagicDec]	⭐️
2024.08	🔥[Speculative Decoding] Boosting Lossless Speculative Decoding via Feature Sampling and Partial Alignment Distillation(@BIT)	[pdf]	⚠️	⭐️⭐️
2024.09	🔥[Hybrid Inference] Efficient Hybrid Inference for LLMs: Reward-Based Token Modelling with Selective Cloud Assistance	[pdf]	⚠️	⭐️⭐️
2024.10	🔥[PARALLELSPEC] PARALLELSPEC: PARALLEL DRAFTER FOR EFFICIENT SPECULATIVE DECODING(@Tencent AI Lab etc)	[pdf]	⚠️	⭐️⭐️
2024.10	🔥[Fast Best-of-N] Fast Best-of-N Decoding via Speculative Rejection(@CMU etc)	[pdf]	⚠️	⭐️⭐️

📖Structured Prune/KD/Weight Sparse (©️back👆🏻)

Date	Title	Paper	Code	Recom
2023.12	[FLAP] Fluctuation-based Adaptive Structured Pruning for Large Language Models(@Chinese Academy of Sciences etc)	[pdf]	[FLAP]	⭐️⭐️
2023.12	🔥[LASER] The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction(@mit.edu)	[pdf]	[laser]	⭐️⭐️
2023.12	[PowerInfer] PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU(@SJTU)	[pdf]	[PowerInfer]	⭐️
2024.01	[Admm Pruning] Fast and Optimal Weight Update for Pruned Large Language Models(@fmph.uniba.sk)	[pdf]	[admm-pruning]	⭐️
2024.01	[FFSplit] FFSplit: Split Feed-Forward Network For Optimizing Accuracy-Efficiency Trade-off in Language Model Inference(@1Rice University etc)	[pdf]	⚠️	⭐️

📖Mixture-of-Experts(MoE) LLM Inference (©️back👆🏻)

Date	Title	Paper	Code	Recom
2022.11	🔥[WINT8/4] Who Says Elephants Can’t Run: Bringing Large Scale MoE Models into Cloud Scale Production(@NVIDIA&Microsoft)	[pdf]	[FasterTransformer]	⭐️⭐️
2023.12	🔥 [Mixtral Offloading] Fast Inference of Mixture-of-Experts Language Models with Offloading(@Moscow Institute of Physics and Technology etc)	[pdf]	[mixtral-offloading]	⭐️⭐️
2024.01	[MoE-Mamba] MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts(@uw.edu.pl)	[pdf]	⚠️	⭐️
2024.04	[MoE Inference] Toward Inference-optimal Mixture-of-Expert Large Language Models(@UC San Diego etc)	[pdf]	⚠️	⭐️
2024.05	🔥🔥🔥[DeepSeek-V2] DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model(@DeepSeek-AI)	[pdf]	[DeepSeek-V2]	⭐️⭐️
2024.06	[MoE] A Survey on Mixture of Experts(@HKU)	[pdf]	⚠️	⭐️

📖CPU/Single GPU/FPGA/NPU/Mobile Inference (©️back👆🏻)

Date	Title	Paper	Code	Recom
2023.03	[FlexGen] High-Throughput Generative Inference of Large Language Models with a Single GPU(@Stanford University etc)	[pdf]	[FlexGen]	⭐️
2023.11	[LLM CPU Inference] Efficient LLM Inference on CPUs(@intel)	[pdf]	[intel-extension-for-transformers]	⭐️
2023.12	[LinguaLinked] LinguaLinked: A Distributed Large Language Model Inference System for Mobile Devices(@University of California Irvine)	[pdf]	⚠️	⭐️
2023.12	[OpenVINO] Leveraging Speculative Sampling and KV-Cache Optimizations Together for Generative AI using OpenVINO(@Haim Barad etc)	[pdf]	⚠️	⭐️
2024.03	[FlightLLM] FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs(@Infinigence-AI)	[pdf]	⚠️	⭐️
2024.03	[Transformer-Lite] Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs(@OPPO)	[pdf]	⚠️	⭐️
2024.07	🔥🔥[xFasterTransformer] Inference Performance Optimization for Large Language Models on CPUs(@Intel)	[pdf]	[xFasterTransformer]	⭐️
2024.07	[Summary] Inference Optimization of Foundation Models on AI Accelerators(@AWS AI)	[pdf]	⚠️	⭐️
2024.10	Large Language Model Performance Benchmarking on Mobile Platforms: A Thorough Evaluation(@SYSU)	[pdf]	⚠️	⭐️
2024.10	🔥🔥[FastAttention] FastAttention: Extend FlashAttention2 to NPUs and Low-resource GPUs for Efficient Inference(@huawei etc)	[pdf]	⚠️	⭐️

📖Non Transformer Architecture (©️back👆🏻)

Date	Title	Paper	Code	Recom
2023.05	🔥🔥[RWKV] RWKV: Reinventing RNNs for the Transformer Era(@Bo Peng etc)	[pdf]	[RWKV-LM]	⭐️⭐️
2023.12	🔥🔥[Mamba] Mamba: Linear-Time Sequence Modeling with Selective State Spaces(@cs.cmu.edu etc)	[pdf]	[mamba]	⭐️⭐️
2024.06	🔥🔥[RWKV-CLIP] RWKV-CLIP: A Robust Vision-Language Representation Learner(@DeepGlint etc)	[pdf]	[RWKV-CLIP]	⭐️⭐️
2024.08	🔥🔥[Kraken] Kraken: Inherently Parallel Transformers For Efficient Multi-Device Inference(@Princeton)	[pdf]	⚠️	⭐️
2024.08	🔥🔥[FLA] FLA: A Triton-Based Library for Hardware-Efficient Implementations of Linear Attention Mechanism(@sustcsonglin)	[docs]	[flash-linear-attention]	⭐️⭐️

📖GEMM/Tensor Cores/MMA/Parallel (©️back👆🏻)

Date	Title	Paper	Code	Recom
2018.03	🔥🔥[Tensor Core] NVIDIA Tensor Core Programmability, Performance & Precision(@KTH Royal etc)	[pdf]	⚠️	⭐️
2021.05	🔥[Intra-SM Parallelism] Exploiting Intra-SM Parallelism in GPUs via Persistent and Elastic Blocks(@sjtu.edu.cn)	[pdf]	⚠️	⭐️
2022.06	[Microbenchmark] Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numeric Behaviors(@tue.nl etc)	[pdf]	[DissectingTensorCores]	⭐️
2022.09	🔥🔥[FP8] FP8 FORMATS FOR DEEP LEARNING(@NVIDIA)	[pdf]	⚠️	⭐️
2023.08	🔥[Tensor Cores] Reducing shared memory footprint to leverage high throughput on Tensor Cores and its flexible API extension library(@Tokyo Institute etc)	[pdf]	[wmma_extension]	⭐️
2023.03	🔥🔥[cutlass/cute] Graphene: An IR for Optimized Tensor Computations on GPUs(@NVIDIA)	[pdf]	[cutlass]	⭐️
2024.02	[QUICK] QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference(@SqueezeBits Inc)	[pdf]	[QUICK]	⭐️⭐️
2024.02	[Tensor Parallel] TP-AWARE DEQUANTIZATION(@IBM T.J. Watson Research Center)	[pdf]	⚠️	⭐️
2024.07	🔥🔥[flute] Fast Matrix Multiplications for Lookup Table-Quantized LLMs(@mit.edu etc)	[pdf]	[flute]	⭐️⭐️
2024.08	🔥🔥[LUT TENSOR CORE] LUT TENSOR CORE: Lookup Table Enables Efficient Low-Bit LLM Inference Acceleration(@SJTU&PKU etc)	[pdf]	⚠️	⭐️
2024.08	🔥🔥[MARLIN] MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models(@ISTA)	[pdf]	[marlin]	⭐️⭐️
2024.08	🔥🔥[SpMM] High Performance Unstructured SpMM Computation Using Tensor Cores(@ETH Zurich)	[pdf]	⚠️	⭐️
2024.09	🔥🔥[TEE]Confidential Computing on nVIDIA H100 GPU: A Performance Benchmark Study(@phala.network)	[pdf]	⚠️	⭐️
2024.09	🔥🔥[HiFloat8] Ascend HiFloat8 Format for Deep Learning(@Huawei)	[pdf]	⚠️	⭐️
2024.09	🔥🔥[Tensor Cores] Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores(@nju.edu.cn)	[pdf]	⚠️	⭐️
2024.07	🔥🔥[Tensor Product] Acceleration of Tensor-Product Operations with Tensor Cores(@Heidelberg University)	[pdf]	⚠️	⭐️

📖VLM/Position Embed/Others (©️back👆🏻)

Date	Title	Paper	Code	Recom
2021.04	🔥[RoPE] ROFORMER: ENHANCED TRANSFORMER WITH ROTARY POSITION EMBEDDING(@Zhuiyi Technology Co., Ltd.)	[pdf]	[transformers]	⭐️
2022.10	[ByteTransformer] A High-Performance Transformer Boosted for Variable-Length Inputs(@ByteDance&NVIDIA)	[pdf]	[ByteTransformer]	⭐️
2024.09	🔥[Inf-MLLM] Inf-MLLM: Efficient Streaming Inference of Multimodal Large Language Models on a Single GPU(@sjtu)	[pdf]	⚠️	⭐️
2024.11	🔥[VL-CACHE] VL-CACHE: SPARSITY AND MODALITY-AWARE KV CACHE COMPRESSION FOR VISION-LANGUAGE MODEL INFERENCE ACCELERATION(@g.ucla.edu etc)	[pdf]	⚠️	⭐️

©️License

GNU General Public License v3.0

🎉Contribute

Welcome to star & submit a PR to this repo!