Awesome LLM Compression

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Awesome LLM compression research papers and tools to accelerate LLM training and inference.

Papers in each category are grouped by year in collapsible blocks, newest first — click a year to expand it. The current year is expanded by default.

Contents

• 📑 Papers
  • Survey
  • Quantization
  • Pruning and Sparsity
  • Distillation
  • Efficient Prompting
  • KV Cache Compression
  • Other
• 🔧 Tools
  • Inference and Serving Engines
  • Quantization Libraries and Kernels
  • Model Compression Toolkits
  • KV Cache and Prompt Compression
  • Training and Fine-Tuning
  • Analysis and Benchmarking
  • Other Tools
• 🙌 Contributing
• 🌟 Star History

Papers

Survey

2026  ·  6 papers

• Dynamic Model Routing and Cascading for Efficient LLM Inference: A Survey
  Arxiv 2026 [Paper]

• A Survey of On-Policy Distillation for Large Language Models
  Arxiv 2026 [Paper]

• Unifying Data, Memory, and Compute Efficiency in LLM training: A Survey
  TAI 2026 [Paper]

• From Tensor Buffer to Distributed Memory Hierarchy: A Survey of KV Cache Management for LLM Serving
  Arxiv 2026 [Paper]

• Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization
  ACL Findings 2026 [Paper]

• Accelerating Masked Diffusion Large Language Models: A Survey of Efficient Inference Techniques
  IJCAI-ECAI 2026 (Survey Track) [Paper]

2025  ·  14 papers

• Compressed but Compromised? A Study of Jailbreaking in Compressed LLMs
  NeurIPS Lock-LLM Workshop 2025 [Paper] [Blog]

• Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systems
  EMNLP 2025 [Paper]

• Key, Value, Compress: A Systematic Exploration of KV Cache Compression Techniques
  CICC 2025 [Paper]

• Are We There Yet? A Measurement Study of Efficiency for LLM Applications on Mobile Devices
  Arxiv 2025 [Paper]

• Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions
  Arxiv 2025 [Paper]

• An Empirical Study on Prompt Compression for Large Language Models
  Building Trust Workshop @ ICLR 2025 2025 [Paper] [PCToolkit]

• Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities
  Arxiv 2025 [Paper] [GitHub Page]

• Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques
  COMPSAC 2025 [Paper]

• A Survey on Inference Engines for Large Language Models: Perspectives on Optimization and Efficiency
  Arxiv 2025 [Paper] [GitHub Page]

• EfficientLLM: Efficiency in Large Language Models
  Arxiv 2025 [Paper] [Homepage] [Huggingface Page]

• KV Cache Compression for Inference Efficiency in LLMs: A Review
  Arxiv 2025 [Paper]

• A Survey on Parallel Text Generation: From Parallel Decoding to Diffusion Language Models
  Arxiv 2025 [Paper] [Code]

• A Survey on Collaborating Small and Large Language Models for Performance, Cost-effectiveness, Cloud-edge Privacy, and Trustworthiness
  Arxiv 2025 [Paper] [Code]

• A Survey on Efficient Large Language Model Training: From Data-centric Perspectives
  ACL 2025 [Paper] [Code]

2024  ·  17 papers

• Understanding LLMs: A Comprehensive Overview from Training to Inference
  Arxiv 2024 [Paper]

• Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward
  IJCAI 2024 (Survey Track) [Paper] [GitHub Page]

• A Survey of Resource-efficient LLM and Multimodal Foundation Models
  Arxiv 2024 [Paper]

• A Survey on Hardware Accelerators for Large Language Models
  Arxiv 2024 [Paper]

• A Comprehensive Survey of Compression Algorithms for Language Models
  Arxiv 2024 [Paper]

• A Survey on Transformer Compression
  Arxiv 2024 [Paper]

• Model Compression and Efficient Inference for Large Language Models: A Survey
  Arxiv 2024 [Paper]

• LLM Inference Unveiled: Survey and Roofline Model Insights
  Arxiv 2024 [Paper]

• A Survey on Knowledge Distillation of Large Language Models
  Arxiv 2024 [Paper] [GitHub Page]

• Efficient Prompting Methods for Large Language Models: A Survey
  Arxiv 2024 [Paper]

• Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application
  Arxiv 2024 [Paper]

• On-Device Language Models: A Comprehensive Review
  Arxiv 2024 [Paper] [Download On-device LLMs]

• A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms
  Arxiv 2024 [Paper]

• Contextual Compression in Retrieval-Augmented Generation for Large Language Models: A Survey
  Arxiv 2024 [Paper]

• Prompt Compression for Large Language Models: A Survey
  Arxiv 2024 [Paper]

• A Comprehensive Study on Quantization Techniques for Large Language Models
  Arxiv 2024 [Paper]

• A Survey on Large Language Model Acceleration based on KV Cache Management
  TMLR 2025 [Paper]

2023  ·  5 papers

• A Survey on Model Compression for Large Language Models
  TACL [Paper]

• The Cost of Compression: Investigating the Impact of Compression on Parametric Knowledge in Language Models
  EMNLP 2023 [Paper] [Code]

• The Efficiency Spectrum of Large Language Models: An Algorithmic Survey
  Arxiv 2023 [Paper]

• Efficient Large Language Models: A Survey
  TMLR [Paper] [GitHub Page]

• Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems
  ICML 2024 Tutorial [Paper] [Tutorial]

Quantization

2026  ·  56 papers

• Can Large Language Models Still Explain Themselves? Investigating the Impact of Quantization on Self-Explanations
  EMNLP Findings 2026 [Paper]

• QSLM: A Performance- and Memory-aware Quantization Framework with Tiered Search Strategy for Spike-driven Language Models
  DATE 2026 [Paper]

• HAS-VQ: Hessian-Adaptive Sparse Vector Quantization for High-Fidelity LLM Compression
  Arxiv 2026 [Paper] [Code]

• ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs
  ACL 2026 [Paper]

• Sherry: Hardware-Efficient 1.25-Bit Ternary Quantization via Fine-grained Sparsification
  Arxiv 2026 [Paper] [Code]

• Calibrating Beyond English: Language Diversity for Better Quantized Multilingual LLM
  EACL 2026 [Paper]

• M2XFP: A Metadata-Augmented Microscaling Data Format for Efficient Low-bit Quantization
  ASPLOS 2026 [Paper]

• Quartet II: Accurate LLM Pre-Training in NVFP4 by Improved Unbiased Gradient Estimation
  Arxiv 2026 [Paper] [Code]

• Two-Stage Grid Optimization for Group-wise Quantization of LLMs
  ICASSP 2026 [Paper]

• Quantized Evolution Strategies: High-precision Fine-tuning of Quantized LLMs at Low-precision Cost
  Arxiv 2026 [Paper] [Code]

• QVLA: Not All Channels Are Equal in Vision-Language-Action Model's Quantization
  ICLR 2026 [Paper]

• TurboBoA: Faster and Exact Attention-aware Quantization without Backpropagation
  ICLR 2026 [Paper]

• RaBiT: Residual-Aware Binarization Training for Accurate and Efficient LLMs
  ICML 2026 [Paper]

• NanoQuant: Efficient Sub-1-Bit Quantization of Large Language Models
  ICML 2026 [Paper]

• On the Importance of a Multi-Scale Calibration for Quantization
  ICASSP 2026 [Paper]

• QTALE: Quantization-Robust Token-Adaptive Layer Execution for LLMs
  ICML 2026 [Paper]

• QuRL: Efficient Reinforcement Learning with Quantized Rollout
  ICLR 2026 [Paper]

• SPQ: An Ensemble Technique for Large Language Model Compression
  LREC 2026 [Paper] [Code]

• Quant Experts: Token-aware Adaptive Error Reconstruction with Mixture of Experts for Large Vision-Language Models Quantization
  CVPR 2026 [Paper]

• MASQuant: Modality-Aware Smoothing Quantization for Multimodal Large Language Models
  CVPR 2026 [Paper]

• SliderQuant: Accurate Post-Training Quantization for LLMs
  ICLR 2026 [Paper] [Code]

• OneComp: One-Line Revolution for Generative AI Model Compression
  Arxiv 2026 [Paper] [Code]

• Fast NF4 Dequantization Kernels for Large Language Model Inference
  ASPLOS 2026 Workshop [Paper]

• RUQuant: Towards Refining Uniform Quantization for Large Language Models
  KDD 2026 [Paper]

• SEPTQ: A Simple and Effective Post-Training Quantization Paradigm for Large Language Models
  KDD 2025 [Paper]

• ReSpinQuant: Efficient Layer-Wise LLM Quantization via Subspace Residual Rotation Approximation
  ICML 2026 [Paper]

• Robust Ultra Low-Bit Post-Training Quantization via Stable Diagonal Curvature Estimate
  MLSys 2026 [Paper]

• AQPIM: Breaking the PIM Capacity Wall for LLMs with In-Memory Activation Quantization
  HPCA 2026 [Paper]

• From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization
  ACL Findings 2026 [Paper]

• Statistically-Lossless Quantization of Large Language Models
  Arxiv 2026 [Paper] [Code]

• EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation
  Arxiv 2026 [Paper] [Code] [Model] [Playground]

• OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization
  ICML 2026 [Paper]

• Normalized Architectures are Natively 4-Bit
  Arxiv 2026 [Paper] [Code]

• RQ-MoE: Residual Quantization via Mixture of Experts for Efficient Input-Dependent Vector Compression
  ICML 2026 [Paper]

• XFP: Quality-Targeted Adaptive Codebook Quantization with Sparse Outlier Separation for LLM Inference
  Arxiv 2026 [Paper] [Code]

• GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
  ICML 2026 [Paper] [Code]

• EVA: Accelerating LLM Decoding via an Efficient Vector Quantization Architecture
  ISCA 2026 [Paper]

• Influence-Inspired Spectral Rotations for Extreme Low-Bit LLM Quantization
  Arxiv 2026 [Paper] [Code]

• InfoQuant: Shaping Activation Distributions for Low-Bit LLM Quantization
  Arxiv 2026 [Paper] [Code]

• LFQ: Logit-aware Final-block Quantization for Boosting the Generation Quality of Low-Bit Quantized LLMs
  ICML 2026 [Paper]

• LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection
  ICML 2026 [Paper]

• UniSVQ: 2-bit Unified Scalar-Vector Quantization
  ICML 2026 [Paper]

• LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization
  ICML 2026 [Paper]

• TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization
  ICML 2026 [Paper]

• CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs
  ICML 2026 [Paper] [Code]

• Quantize the Target, Quantize the Drafter: Efficient Inference with Qwen3.5-4B
  Arxiv 2026 [Paper] [Code]

• KronQ: LLM Quantization via Kronecker-Factored Hessian
  COLM 2026 [Paper]

• Rethinking Small VLM Quantization: From Component-Wise Analysis to Hardware-Aware Edge Deployment
  ICML 2026 Workshop [Paper]

• Reliability Scaling Laws for Quantized Large Language Models
  TMLR 2026 [Paper]

• PolyQ: Codesigning End-to-End Quantization Framework for Scalable Edge CPU LLM Inference
  ICCAD 2026 [Paper]

• ExaGEMM: Exploration Framework for CPU-Driven ML Inference via Associative In-Register Computing for Low-Bit GEMM
  ICCAD 2026 [Paper]

• GyRot: Leveraging Hidden Synergy between Rotation and Fine-grained Group Quantization for Low-bit LLM Inference
  HPCA 2026 [Paper]

• Attend to Your Own Thoughts: Breaking the Barrier for Post-Training Quantization of Reasoning LLMs through the Lens of 1.58-Bit Quantization
  Arxiv 2026 [Paper] [Code]

• ARCHead: Activation-Metric Residual Correction for Large Language Model Output Heads
  Arxiv 2026 [Paper] [Code]

• Compress and Forget: bitsandbytes Quantization Amplifies Proactive Interference in LLMs
  Arxiv 2026 [Paper] [Code]

• Jacobian-guided Noise Injection for Quantization Robustness in Large Language Models
  ICML 2026 [Paper]

2025  ·  118 papers

• HALO: Hadamard-Assisted Lower-Precision Optimization for LLMs
  Arxiv 2025 [Paper]

• RaZeR: Pushing the Limits of NVFP4 Quantization with Redundant Zero Remapping
  Arxiv 2025 [Paper]

• FlexQuant: Elastic Quantization Framework for Locally Hosted LLM on Edge Devices
  Arxiv 2025 [Paper]

• Rethinking Post-Training Quantization: Introducing a Statistical Pre-Calibration Approach
  Arxiv 2025 [Paper]

• Qrazor: Reliable and effortless 4-bit llm quantization by significant data razoring
  Arxiv 2025 [Paper]

• OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting
  ICLR 2025 [Paper] [Code]

• SwiftPrune: Hessian-Free Weight Pruning for Large Language Models
  Arxiv 2025 [Paper]

• Progressive Binarization with Semi-Structured Pruning for LLMs
  Arxiv 2025 [Paper] [Code]

• Physics-Inspired Binary Neural Networks: Interpretable Compression with Theoretical Guarantees
  Arxiv 2025 [Paper]

• QuEST: Stable Training of LLMs with 1-Bit Weights and Activations
  Arxiv 2025 [Paper]

• ParetoQ: Scaling Laws in Extremely Low-bit LLM Quantization
  NeurIPS 2025 [Paper]

• Systematic Outliers in Large Language Models
  ICLR 2025 [Paper] [Code]

• Can Post-Training Quantization Benefit from an Additional QLoRA Integration?
  NAACL 2025 [Paper]

• 1bit-Merging: Dynamic Quantized Merging for Large Language Models
  Arxiv 2025 [Paper]

• Towards Efficient Pre-training: Exploring FP4 Precision in Large Language Models
  Arxiv 2025 [Paper]

• Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models?
  Arxiv 2025 [Paper]

• QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models
  Arxiv 2025 [Paper]

• Benchmarking Post-Training Quantization in LLMs: Comprehensive Taxonomy, Unified Evaluation, and Comparative Analysis
  Arxiv 2025 [Paper]

• Compression Scaling Laws:Unifying Sparsity and Quantization
  Arxiv 2025 [Paper]

• M-ANT: Efficient Low-bit Group Quantization for LLMs via Mathematically Adaptive Numerical Type
  Arxiv 2025 [Paper]

• Identifying Sensitive Weights via Post-quantization Integral
  Arxiv 2025 [Paper]

• RSQ: Learning from Important Tokens Leads to Better Quantized LLMs
  Arxiv 2025 [Paper] [Code]

• VQ-LLM: High-performance Code Generation for Vector Quantization Augmented LLM Inference
  HPCA 2025 [Paper]

• Universality of Layer-Level Entropy-Weighted Quantization Beyond Model Architecture and Size
  Arxiv 2025 [Paper]

• Towards Superior Quantization Accuracy: A Layer-sensitive Approach
  Arxiv 2025 [Paper]

• MergeQuant: Accurate 4-bit Static Quantization of Large Language Models by Channel-wise Calibration
  Arxiv 2025 [Paper]

• ClusComp: A Simple Paradigm for Model Compression and Efficient Finetuning
  Arxiv 2025 [Paper]

• DynaMo: Runtime Switchable Quantization for MoE with Cross-Dataset Adaptation
  DATE 2026 [Paper]

• Cocktail: Chunk-Adaptive Mixed-Precision Quantization for Long-Context LLM Inference
  DATE 2025 [Paper]

• GPTQv2: Efficient Finetuning-Free Quantization for Asymmetric Calibration
  ICML 2025 [Paper] [Code]

• Task-Circuit Quantization: Leveraging Knowledge Localization and Interpretability for Compression
  COLM 2025 [Paper] [Code]

• Quantization Error Propagation: Revisiting Layer-Wise Post-Training Quantization
  NeurIPS 2025 [Paper] [Code]

• RaanA: A Fast, Flexible, and Data-Efficient Post-Training Quantization Algorithm
  Arxiv 2025 [Paper] [Code]

• Achieving binary weight and activation for LLMs using Post-Training Quantization
  Arxiv 2025 [Paper]

• DL-QAT: Weight-Decomposed Low-Rank Quantization-Aware Training for Large Language Models
  EMNLP 2024 [Paper]

• Gradual Binary Search and Dimension Expansion : A general method for activation quantization in LLMs
  Arxiv 2025 [Paper]

• FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference
  Arxiv 2025 [Paper]

• BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs
  Arxiv 2025 [Paper]

• FineQ: Software-Hardware Co-Design for Low-Bit Fine-Grained Mixed-Precision Quantization of LLMs
  DATE 2025 [Paper]

• Precision Where It Matters: A Novel Spike Aware Mixed-Precision Quantization Strategy for LLaMA-based Language Models
  Arxiv 2025 [Paper]

• ICQuant: Index Coding enables Low-bit LLM Quantization
  Arxiv 2025 [Paper]

• Radio: Rate-Distortion Optimization for Large Language Model Compression
  ICML 2025 [Paper]

• Balancing Fidelity and Plasticity: Aligning Mixed-Precision Fine-Tuning with Linguistic Hierarchies
  Arxiv 2025 [Paper]

• MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance
  ICML 2025 [Paper]

• Grouped Sequency-arranged Rotation: Optimizing Rotation Transformation for Quantization for Free
  Arxiv 2025 [Paper]

• Improving Block-Wise LLM Quantization by 4-bit Block-Wise Optimal Float (BOF4): Analysis and Variations
  Arxiv 2025 [Paper]

• GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance
  ICML 2025 [Paper] [Code]

• QuantX: A Framework for Hardware-Aware Quantization of Generative AI Workloads
  Arxiv 2025 [Paper]

• An Extra RMSNorm is All You Need for Fine Tuning to 1.58 Bits
  Arxiv 2025 [Paper]

• ITERA-LLM: Boosting Sub-8-Bit Large Language Model Inference via Iterative Tensor Decomposition
  Arxiv 2025 [Paper]

• Fine-tuning Quantized Neural Networks with Zeroth-order Optimization
  ICLR 2026 [Paper]

• Scaling Law for Quantization-Aware Training
  Arxiv 2025 [Paper]

• Quaff: Quantized Parameter-Efficient Fine-Tuning under Outlier Spatial Stability Hypothesis
  ACL 2025 [Paper] [Code]

• Is (Selective) Round-To-Nearest Quantization All You Need?
  Arxiv 2025 [Paper]

• NeUQI: Near-Optimal Uniform Quantization Parameter Initialization
  ICML 2026 [Paper]

• LoTA-QAF: Lossless Ternary Adaptation for Quantization-Aware Fine-Tuning
  Arxiv 2025 [Paper] [Code]

• FP4 All the Way: Fully Quantized Training of LLMs
  Arxiv 2025 [Paper] [Code]

• FireQ: Fast INT4-FP8 Kernel and RoPE-aware Quantization for LLM Inference Acceleration
  Arxiv 2025 [Paper]

• Rethinking the Outlier Distribution in Large Language Models: An In-depth Study
  Arxiv 2025 [Paper]

• Assigning Distinct Roles to Quantized and Low-Rank Matrices Toward Optimal Weight Decomposition
  ACL Findings 2025 [Paper]

• Unifying Uniform and Binary-coding Quantization for Accurate Compression of Large Language Models
  ACL 2025 [Paper]

• FPTQuant: Function-Preserving Transforms for LLM Quantization
  ICML 2026 [Paper]

• BAQ: Efficient Bit Allocation Quantization for Large Language Models
  Arxiv 2025 [Paper] [Code]

• MoQAE: Mixed-Precision Quantization for Long-Context LLM Inference via Mixture of Quantization-Aware Experts
  ACL 2025 [Paper]

• Unifying Block-wise PTQ and Distillation-based QAT for Progressive Quantization toward 2-bit Instruction-Tuned LLMs
  Arxiv 2025 [Paper]

• Boost Post-Training Quantization via Null Space Optimization for Large Language Models
  Arxiv 2025 [Paper] [Code]

• FlexQuant: A Flexible and Efficient Dynamic Precision Switching Framework for LLM Quantization
  Arxiv 2025 [Paper]

• BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook
  Arxiv 2025 [Paper]

• ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models
  Arxiv 2025 [Paper]

• LittleBit: Ultra Low-Bit Quantization via Latent Factorization
  NeurIPS 2025 [Paper]

• BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models
  Arxiv 2025 [Paper] [Code]

• UltraSketchLLM: Sub-1-Bit LLM Compression via Sketch and Hardware-Friendly Operators
  DAC 2026 [Paper]

• DBellQuant: Breaking the Bell with Double-Bell Transformation for LLMs Post Training Binarization
  Arxiv 2025 [Paper]

• any4: Learned 4-bit Numeric Representation for LLMs
  ICML 2025 [Paper]

• CCQ: Convolutional Code for Extreme Low-bit Quantization in LLMs
  Arxiv 2025 [Paper]

• First-Order Error Matters: Accurate Compensation for Quantized Large Language Models
  AAAI 2026 [Paper]

• PoTPTQ: A Two-step Power-of-Two Post-training for LLMs
  ECAI 2025 [Paper]

• EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models
  ACL 2025 [Paper]

• MicroMix: Efficient Mixed-Precision Quantization with Microscaling Formats for Large Language Models
  Arxiv 2025 [Paper] [Code]

• VLMQ: Token Saliency-Driven Post-Training Quantization for Vision-language Models
  Arxiv 2025 [Paper]

• FlashCommunication V2: Bit Splitting and Spike Reserving for Any Bit Communication
  Arxiv 2025 [Paper]

• FlexQ: Efficient Post-training INT6 Quantization for LLM Serving via Algorithm-System Co-Design
  Arxiv 2025 [Paper] [Code]

• Provable Post-Training Quantization: Theoretical Analysis of OPTQ and Qronos
  Arxiv 2025 [Paper]

• iFairy: the First 2-bit Complex LLM with All Parameters in ${\pm1, \pm i}$
  Arxiv 2025 [Paper]

• Pushing the Envelope of LLM Inference on AI-PC and Intel GPUs
  Arxiv 2025 [Paper]

• Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models
  Arxiv 2025 [Paper]

• Efficient Edge LLMs Deployment via HessianAware Quantization and CPU GPU Collaborative
  Arxiv 2025 [Paper]

• Profiling Large Language Model Inference on Apple Silicon: A Quantization Perspective
  Arxiv 2025 [Paper]

• LLM Compression: How Far Can We Go in Balancing Size and Performance?
  RANLP 2025 [Paper]

• DLLMQuant: Quantizing Diffusion-based Large Language Models
  Arxiv 2025 [Paper]

• Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs
  Machine Intelligence Research 2025 [Paper]

• Systematic Characterization of LLM Quantization: A Performance, Energy, and Quality Perspective
  Arxiv 2025 [Paper]

• Interpreting the Effects of Quantization on LLMs
  AACL 2025 [Paper]

• Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models
  ACL Findings 2026 [Paper]

• APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration
  TCAD 2025 [Paper]

• Quantized but Deceptive? A Multi-Dimensional Truthfulness Evaluation of Quantized LLMs
  EMNLP 2025 [Paper]

• The Uneven Impact of Post-Training Quantization in Machine Translation
  Arxiv 2025 [Paper]

• BitROM: Weight Reload-Free CiROM Architecture Towards Billion-Parameter 1.58-bit LLM Inference
  ASP-DAC 2026 [Paper]

• AMQ: Enabling AutoML for Mixed-precision Weight-Only Quantization of Large Language Models
  EMNLP 2025 [Paper]

• Fair-GPTQ: Bias-Aware Quantization for Large Language Models
  Arxiv 2025 [Paper]

• QWHA: Quantization-Aware Walsh-Hadamard Adaptation for Parameter-Efficient Fine-Tuning on Large Language Models
  ICLR 2026 [Paper]

• Q-Palette: Fractional-Bit Quantizers Toward Optimal Bit Allocation for Efficient LLM Deployment
  NeurIPS 2025 [Paper]

• AnyBCQ: Hardware Efficient Flexible Binary-Coded Quantization for Multi-Precision LLMs
  ICLR 2026 [Paper]

• QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
  Arxiv 2025 [Paper] [Code]

• F-BFQ: Flexible Block Floating-Point Quantization Accelerator for LLMs
  ISCA 2025 Workshop [Paper]

• Learning Grouped Lattice Vector Quantizers for Low-Bit LLM Compression
  NeurIPS 2025 [Paper]

• A Convergence Analysis of Adaptive Optimizers under Floating-point Quantization
  ICLR 2026 [Paper]

• FALQON: Accelerating LoRA Fine-tuning with Low-Bit Floating-Point Arithmetic
  NeurIPS 2025 [Paper]

• TetraJet-v2: Accurate NVFP4 Training for Large Language Models with Oscillation Suppression and Outlier Control
  ICML 2026 [Paper]

• DartQuant: Efficient Rotational Distribution Calibration for LLM Quantization
  NeurIPS 2025 [Paper]

• You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations
  ICML 2026 Workshop [Paper]

• P3-LLM: An Integrated NPU-PIM Accelerator for Edge LLM Inference Using Hybrid Numerical Formats
  ISCA 2026 [Paper]

• ParoQuant: Pairwise Rotation Quantization for Efficient Reasoning LLM Inference
  ICLR 2026 [Paper] [Code]

• SpecQuant: Spectral Decomposition and Adaptive Truncation for Ultra-Low-Bit LLMs Quantization
  AAAI 2026 [Paper]

• T-SAR: A Full-Stack Co-design for CPU-Only Ternary LLM Inference via In-Place SIMD ALU Reorganization
  DATE 2026 [Paper]

• Enhancing Trustworthiness with Mixed Precision: Benchmarks, Opportunities, and Challenges
  ASP-DAC 2026 [Paper]

• SignRoundV2: Toward Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs
  Arxiv 2025 [Paper] [Code]

• CodeGEMM: A Codebook-Centric Approach to Efficient GEMM in Quantized LLMs
  NeurIPS 2025 [Paper]

2024  ·  162 papers

• FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGA
  FPGA 2024 [Paper]

• Extreme Compression of Large Language Models via Additive Quantization
  ICML 2024 [Paper] [Code]

• Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models
  Arxiv 2024 [Paper]

• Inferflow: an Efficient and Highly Configurable Inference Engine for Large Language Models
  Arxiv 2024 [Paper]

• FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design
  USENIX ATC 2024 [Paper]

• Can Large Language Models Understand Context?
  EACL Findings 2024 [Paper]

• Squat: Quant Small Language Models on the Edge
  ICCAD 2025 [Paper] [Code]

• LQER: Low-Rank Quantization Error Reconstruction for LLMs
  ICML 2024 [Paper]

• BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
  Arxiv 2024 [Paper] [Code]

• QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks
  ICML 2024 [Paper] [Code]

• L4Q: Parameter Efficient Quantization-Aware Training on Large Language Models via LoRA-wise LSQ
  Arxiv 2024 [Paper]

• TP-Aware Dequantization
  Arxiv 2024 [Paper]

• ApiQ: Finetuning of 2-Bit Quantized Large Language Model
  EMNLP 2024 [Paper]

• Accurate LoRA-Finetuning Quantization of LLMs via Information Retention
  Arxiv 2024 [Paper] [Code]

• BitDelta: Your Fine-Tune May Only Be Worth One Bit
  NeurIPS 2024 [Paper] [Code]

• QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large Language Model Tuning
  EMNLP 2024 Industry Track [Paper]

• Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs
  ICML 2024 [Paper]

• BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation
  ACL 2024 [Paper] [Code]

• OneBit: Towards Extremely Low-bit Large Language Models
  NeurIPS 2024 [Paper]

• DB-LLM: Accurate Dual-Binarization for Efficient LLMs
  ACL Findings 2024 [Paper]

• WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More
  Arxiv 2024 [Paper]

• GPTVQ: The Blessing of Dimensionality for LLM Quantization
  Arxiv 2024 [Paper] [Code]

• APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models
  DAC 2024 [Paper]

• A Comprehensive Evaluation of Quantization Strategies for Large Language Models
  ACL Findings 2024 [Paper]

• Evaluating Quantized Large Language Models
  Arxiv 2024 [Paper]

• FlattenQuant: Breaking Through the Inference Compute-bound for Large Language Models with Per-tensor Quantization
  Arxiv 2024 [Paper]

• LLM-PQ: Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization
  Arxiv 2024 [Paper]

• IntactKV: Improving Large Languagze Model Quantization by Keeping Pivot Tokens Intact
  ACL Findings 2024 [Paper] [Code]

• On the Compressibility of Quantized Large Language Models
  Arxiv 2024 [Paper]

• EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs
  Arxiv 2024 [Paper]

• What Makes Quantization for Large Language Models Hard? An Empirical Study from the Lens of Perturbation
  Arxiv 2024 [Paper]

• AffineQuant: Affine Transformation Quantization for Large Language Models
  ICLR 2024 [Paper] [Code]

• Oh! We Freeze: Improving Quantized Knowledge Distillation via Signal Propagation Analysis for Large Language Models
  ICLR Practical ML for Low Resource Settings Workshop 2024 [Paper]

• Accurate Block Quantization in LLMs with Outliers
  Arxiv 2024 [Paper]

• QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs
  Arxiv 2024 [Paper] [Code]

• Minimize Quantization Output Error with Bias Compensation
  Arxiv 2024 [Paper] [Code]

• Cherry on Top: Parameter Heterogeneity and Quantization in Large Language Models
  Arxiv 2024 [Paper]

• Fine-Tuning, Quantization, and LLMs: Navigating Unintended Outcomes
  Arxiv 2024 [Paper]

• Quantization of Large Language Models with an Overdetermined Basis
  Arxiv 2024 [Paper]

• An empirical study of LLaMA3 quantization: from LLMs to MLLMs
  Arxiv 2024 [Paper] [Code]

• How to Parameterize Asymmetric Quantization Ranges for Quantization-Aware Training
  Arxiv 2024 [Paper]

• Mitigating the Impact of Outlier Channels for Language Model Quantization with Activation Regularization
  Arxiv 2024 [Paper] [Code]

• When Quantization Affects Confidence of Large Language Models?
  NAACL 2024 [Paper]

• QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving
  Arxiv 2024 [Paper] [Code]

• Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLMs
  ICML 2024 [Paper]

• LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit
  EMNLP 2024 [Paper] [Code]

• SKVQ: Sliding-window Key and Value Cache Quantization for Large Language Models
  Arxiv 2024 [Paper]

• Post Training Quantization of Large Language Models with Microscaling Formats
  Arxiv 2024 [Paper]

• Edge Intelligence Optimization for Large Language Model Inference with Batching and Quantization
  Arxiv 2024 [Paper]

• SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models
  Arxiv 2024 [Paper] [Code]

• OAC: Output-adaptive Calibration for Accurate Post-training Quantization
  Arxiv 2024 [Paper]

• PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression
  Arxiv 2024 [Paper]

• SpinQuant -- LLM quantization with learned rotations
  ICLR 2025 [Paper]

• Compressing Large Language Models using Low Rank and Low Precision Decomposition
  NeurIPS 2024 [Paper] [Code]

• Athena: Efficient Block-Wise Post-Training Quantization for Large Language Models Using Second-Order Matrix Derivative Information
  Arxiv 2024 [Paper]

• Exploiting LLM Quantization
  Arxiv 2024 [Paper]

• One QuantLLM for ALL: Fine-tuning Quantized LLMs Once for Efficient Deployments
  Arxiv 2024 [Paper]

• LCQ: Low-Rank Codebook based Quantization for Large Language Models
  Arxiv 2024 [Paper]

• LoQT: Low Rank Adapters for Quantized Training
  Arxiv 2024 [Paper] [Code]

• CLAQ: Pushing the Limits of Low-Bit Post-Training Quantization for LLMs
  Arxiv 2024 [Paper]

• I-LLM: Efficient Integer-Only Inference for Fully-Quantized Low-Bit Large Language Models
  Arxiv 2024 [Paper]

• Outliers and Calibration Sets have Diminishing Effect on Quantization of Modern LLMs
  Arxiv 2024 [Paper]

• DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs
  NeurIPS 2024 [Paper] [Code]

• ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization
  NeurIPS 2024 [Paper] [Code]

• Low-Rank Quantization-Aware Training for LLMs
  Arxiv 2024 [Paper]

• TernaryLLM: Ternarized Large Language Model
  Arxiv 2024 [Paper]

• Examining Post-Training Quantization for Mixture-of-Experts: A Benchmark
  Arxiv 2024 [Paper] [Code]

• Delta-CoMe: Training-Free Delta-Compression with Mixed-Precision for Large Language Models
  NeurIPS 2024 [Paper]

• QQQ: Quality Quattuor-Bit Quantization for Large Language Models
  Arxiv 2024 [Paper] [Code]

• QTIP: Quantization with Trellises and Incoherence Processing
  NeurIPS 2024 [Paper] [Code]

• Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization
  EMNLP 2024 [Paper]

• Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for Large Language Models
  NeurIPS 2024 [Paper]

• Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization
  ISCA 2024 [Paper]

• SDQ: Sparse Decomposed Quantization for LLM Inference
  Arxiv 2024 [Paper]

• Attention-aware Post-training Quantization without Backpropagation
  ICML 2025 [Paper]

• EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and Voting
  Arxiv 2024 [Paper] [Code]

• Compensate Quantization Errors: Make Weights Hierarchical to Compensate Each Other
  Arxiv 2024 [Paper]

• Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels
  Arxiv 2024 [Paper] [Code]

• CDQuant: Greedy Coordinate Descent for Accurate LLM Quantization
  Arxiv 2024 [Paper]

• OutlierTune: Efficient Channel-Wise Quantization for Large Language Models
  Arxiv 2024 [Paper]

• T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge
  EuroSys 2025 [Paper] [Code]

• GPTQT: Quantize Large Language Models Twice to Push the Efficiency
  ICORIS 2024 [Paper]

• Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment
  ACL 2024 [Paper]

• How Does Quantization Affect Multilingual LLMs?
  EMNLP Findings 2024 [Paper]

• RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization
  EMNLP Findings 2024 [Paper] [Code]

• Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients
  Arxiv 2024 [Paper] [Code]

• FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision
  Arxiv 2024 [Paper] [Code]

• Accuracy is Not All You Need
  Arxiv 2024 [Paper]

• BitNet b1.58 Reloaded: State-of-the-art Performance Also on Smaller Networks
  Arxiv 2024 [Paper]

• LeanQuant: Accurate Large Language Model Quantization with Loss-Error-Aware Grid
  ICLR 2025 [Paper]

• Fast Matrix Multiplications for Lookup Table-Quantized LLMs
  EMNLP Findings 2024 [Paper] [Code]

• EfficientQAT: Efficient Quantization-Aware Training for Large Language Models
  ACL 2025 [Paper] [Code]

• LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices
  NAACL 2025 [Paper]

• Exploring Quantization for Efficient Pre-Training of Transformer Language Models
  EMNLP Findings 2024 [Paper] [Code]

• Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale
  Arxiv 2024 [Paper] [Code]

• Mamba-PTQ: Outlier Channels in Recurrent Large Language Models
  Efficient Systems for Foundation Models Workshop @ ICML 2024 [Paper]

• Compensate Quantization Errors+: Quantized Models Are Inquisitive Learners
  Arxiv 2024 [Paper]

• Accurate and Efficient Fine-Tuning of Quantized Large Language Models Through Optimal Balance
  Arxiv 2024 [Paper] [Code]

• STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs
  Arxiv 2024 [Paper]

• Advancing Multimodal Large Language Models with Quantization-Aware Scale Learning for Efficient Adaptation
  ACM MM 2024 [Paper]

• ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models
  AAAI 2025 [Paper]

• MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models
  Arxiv 2024 [Paper] [Code (Marlin)] [Code (Sparse Marlin)]

• Matmul or No Matmal in the Era of 1-bit LLMs
  Arxiv 2024 [Paper]

• MobileQuant: Mobile-friendly Quantization for On-device Language Models
  EMNLP Findings 2024 [Paper] [Code]

• GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs
  Arxiv 2024 [Paper] [Code]

• Foundations of Large Language Model Compression -- Part 1: Weight Quantization
  Arxiv 2024 [Paper]

• OPAL: Outlier-Preserved Microscaling Quantization A ccelerator for Generative Large Language Models
  DAC 2024 [Paper]

• VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models
  EMNLP 2024 [Paper] [Code]

• Scaling FP8 training to trillion-token LLMs
  Arxiv 2024 [Paper]

• Accumulator-Aware Post-Training Quantization
  Arxiv 2024 [Paper]

• Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores
  ASP-DAC 2025 [Paper]

• Rotated Runtime Smooth: Training-Free Activation Smoother for accurate INT4 inference
  Arxiv 2024 [Paper] [Code]

• EXAQ: Exponent Aware Quantization For LLMs Acceleration
  Arxiv 2024 [Paper]

• ARB-LLM: Alternating Refined Binarizations for Large Language Models
  Arxiv 2024 [Paper] [Code]

• PrefixQuant: Eliminating Outliers by Prefixed Tokens for Large Language Models Quantization
  Arxiv 2024 [Paper] [Code]

• Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation
  ICML 2025 [Paper]

• Scaling Laws For Mixed Quantization
  Arxiv 2024 [Paper]

• Q-VLM: Post-training Quantization for Large Vision-Language Models
  NeurIPS 2024 [Paper] [Code]

• CrossQuant: A Post-Training Quantization Method with Smaller Quantization Kernel for Precise Large Language Model Compression
  Arxiv 2024 [Paper]

• FlatQuant: Flatness Matters for LLM Quantization
  ICML 2025 [Paper] [Code]

• DeltaDQ: Ultra-High Delta Compression for Fine-Tuned LLMs via Group-wise Dropout and Separate Quantization
  Arxiv 2024 [Paper]

• QEFT: Quantization for Efficient Fine-Tuning of LLMs
  EMNLP Findings 2024 [Paper] [Code]

• Continuous Approximations for Improving Quantization Aware Training of LLMs
  Arxiv 2024 [Paper]

• DAQ: Density-Aware Post-Training Weight-Only Quantization For LLMs
  Arxiv 2024 [Paper]

• COMET: Towards Partical W4A4KV4 LLMs Serving
  Arxiv 2024 [Paper]

• Scaling laws for post-training quantized large language models
  Arxiv 2024 [Paper]

• Channel-Wise Mixed-Precision Quantization for Large Language Models
  Arxiv 2024 [Paper]

• Understanding the difficulty of low-precision post-training quantization of large language models
  Arxiv 2024 [Paper]

• QuAILoRA: Quantization-Aware Initialization for LoRA
  NeurIPS Workshop on Efficient Natural Language and Speech Processing (ENLSP-IV) 2024 [Paper]

• SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training
  NeurIPS 2024 [Paper]

• Pyramid Vector Quantization for LLMs
  Arxiv 2024 [Paper]

• TesseraQ: Ultra Low-Bit LLM Post-Training Quantization with Block Reconstruction
  Arxiv 2024 [Paper] [Code]

• COAT: Compressing Optimizer states and Activation for Memory-Efficient FP8 Training
  ICLR 2025 [Paper] [Code]

• GWQ: Gradient-Aware Weight Quantization for Large Language Models
  Arxiv 2024 [Paper]

• "Give Me BF16 or Give Me Death"? Accuracy-Performance Trade-Offs in LLM Quantization
  ACL 2025 [Paper]

• Interactions Across Blocks in Post-Training Quantization of Large Language Models
  Arxiv 2024 [Paper]

• BitNet a4.8: 4-bit Activations for 1-bit LLMs
  Arxiv 2024 [Paper]

• The Super Weight in Large Language Models
  Arxiv 2024 [Paper] [Code]

• ASER: Activation Smoothing and Error Reconstruction for Large Language Model Quantization
  AAAI 2025 [Paper]

• Towards Low-bit Communication for Tensor Parallel LLM Inference
  Arxiv 2024 [Paper]

• AMXFP4: Taming Activation Outliers with Asymmetric Microscaling Floating-Point for 4-bit LLM Inference
  Arxiv 2024 [Paper] [Code]

• Scaling Laws for Precision
  Arxiv 2024 [Paper]

• BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration
  HPCA 2025 [Paper] [Code]

• SageAttention2: Efficient Attention with Thorough Outlier Smoothing and Per-thread INT4 Quantization
  ICML 2025 [Paper] [Code]

• AutoMixQ: Self-Adjusting Quantization for High Performance Memory-Efficient Fine-Tuning
  Arxiv 2024 [Paper]

• Anda: Unlocking Efficient LLM Inference with a Variable-Length Grouped Activation Data Format
  HPCA 2025 [Paper]

• MixPE: Quantization and Hardware Co-design for Efficient LLM Inference
  Arxiv 2024 [Paper]

• Pushing the Limits of Large Language Model Quantization via the Linearity Theorem
  Arxiv 2024 [Paper]

• Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens
  Arxiv 2024 [Paper] [Models]

• DFRot: Achieving Outlier-Free and Massive Activation-Free for Rotated LLMs with Refined Rotation
  COLM 2025 [Paper] [Code]

• RILQ: Rank-Insensitive LoRA-based Quantization Error Compensation for Boosting 2-bit Large Language Model Accuracy
  AAAI 2025 [Paper]

• CPTQuant -- A Novel Mixed Precision Post-Training Quantization Techniques for Large Language Models
  Arxiv 2024 [Paper]

• SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization
  Arxiv 2024 [Paper]

• Direct Quantized Training of Language Models with Stochastic Rounding
  Arxiv 2024 [Paper] [Code]

• Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization
  Arxiv 2024 [Paper]

• Low-Rank Correction for Quantized LLMs
  Arxiv 2024 [Paper]

• CRVQ: Channel-relaxed Vector Quantization for Extreme Compression of LLMs
  Arxiv 2024 [Paper]

• ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals
  Arxiv 2024 [Paper] [Code]

• MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design
  MLSys 2026 [Paper]

• GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference
  Arxiv 2024 [Paper]

• LSAQ: Layer-Specific Adaptive Quantization for Large Language Model Deployment
  Arxiv 2024 [Paper]

• DecDEC: A Systems Approach to Advancing Low-Bit LLM Quantization
  OSDI 2025 [Paper]

2023  ·  75 papers

• FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization
  ICML 2023 [Paper] [Code (DeepSpeed)]

• Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases
  ICML 2023 [Paper] [Code]

• The case for 4-bit precision: k-bit Inference Scaling Laws
  ICML 2023 [Paper]

• PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models
  ACL 2023 [Paper]

• Boost Transformer-based Language Models with GPU-Friendly Sparsity and Quantization
  ACL 2023 [Paper]

• QLoRA: Efficient Finetuning of Quantized LLMs
  NeurIPS 2023 [Paper] [Code]

• The Quantization Model of Neural Scaling
  NeurIPS 2023 [Paper]

• Quantized Distributed Training of Large Models with Convergence Guarantees
  ICML 2023 [Paper]

• RPTQ: Reorder-based Post-training Quantization for Large Language Models
  Arxiv 2023 [Paper] [Code]

• ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation
  AAAI 2024 [Paper] [Code]

• Integer or Floating Point? New Outlooks for Low-Bit Quantization on Large Language Models
  ICML 2024 [Paper]

• Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization
  NeurIPS 2023 [Paper]

• Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt
  Arxiv 2023 [Paper]

• AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
  MLSys 2024 (Best Paper 🏆) [Paper] [Code]

• LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
  ACL Findings 2024 [Paper] [Code]

• SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression
  ICLR 2024 [Paper] [Code]

• OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
  AAAI 2024 [Paper]

• SqueezeLLM: Dense-and-Sparse Quantization
  ICML 2024 [Paper] [Code]

• INT2.1: Towards Fine-Tunable Quantized Large Language Models with Error Correction through Low-Rank Adaptation
  Arxiv 2023 [Paper]

• LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model Finetuning
  ICLR 2024 [Paper]

• INT-FP-QSim: Mixed Precision and Formats For Large Language Models and Vision Transformers
  Arxiv 2023 [Paper] [Code]

• QIGen: Generating Efficient Kernels for Quantized Inference on Large Language Models
  Arxiv 2023 [Paper] [Code]

• Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study
  COLING 2024 [Paper]

• ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats
  Arxiv 2023 [Paper] [Code (DeepSpeed)]

• OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization
  ISCA 2023 [Paper]

• NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search
  Arxiv 2023 [Paper]

• GPT-Zip: Deep Compression of Finetuned Large Language Models
  ICML 2023 Workshop ES-FoMO [Paper]

• Generating Efficient Kernels for Quantized Inference on Large Language Models
  ICML 2023 Workshop ES-FoMO [Paper]

• Gradient-Based Post-Training Quantization: Challenging the Status Quo
  Arxiv 2023 [Paper]

• FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs
  Arxiv 2023 [Paper]

• OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
  ICLR 2024 [Paper] [Code]

• FPTQ: Fine-grained Post-Training Quantization for Large Language Models
  Arxiv 2023 [Paper]

• eDKM: An Efficient and Accurate Train-time Weight Clustering for Large Language Models
  IEEE Computer Architecture Letters 2023 [Paper]

• QuantEase: Optimization-based Quantization for Language Models -- An Efficient and Intuitive Algorithm
  Arxiv 2023 [Paper]

• Norm Tweaking: High-performance Low-bit Quantization of Large Language Models
  AAAI 2024 [Paper]

• Understanding the Impact of Post-Training Quantization on Large-scale Language Models
  Arxiv 2023 [Paper]

• MEMORY-VQ: Compression for Tractable Internet-Scale Memory
  NAACL 2024 [Paper]

• Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs
  EMNLP Findings 2024 [Paper] [Code]

• Efficient Post-training Quantization with FP8 Formats
  MLSys 2024 [Paper] [Code (Intel® Neural Compressor)]

• QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
  ICLR 2024 [Paper] [Code]

• Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models
  ICLR 2024 [Paper] [Code]

• ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
  TMLR (Featured Certification 🌟) [Paper]

• PB-LLM: Partially Binarized Large Language Models
  ICLR 2024 [Paper] [Code]

• Dual Grained Quantization: Efficient Fine-Grained Quantization for LLM
  Arxiv 2023 [Paper]

• QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models
  ICLR 2024 [Paper] [Code]

• LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
  ICLR 2024 [Paper] [Code]

• QFT: Quantized Full-parameter Tuning of LLMs with Affordable Resources
  ICLR 2026 Workshop [Paper]

• TEQ: Trainable Equivalent Transformation for Quantization of LLMs
  Arxiv 2023 [Paper] [Code (Intel® Neural Compressor)]

• BitNet: Scaling 1-bit Transformers for Large Language Models
  Arxiv 2023 [Paper] [Code]

• FP8-LM: Training FP8 Large Language Models
  Arxiv 2023 [Paper] [Code]

• QUIK: Towards End-to-End 4-Bit Inference on Generative Large Language Models
  EMNLP 2024 [Paper] [Code]

• AFPQ: Asymmetric Floating Point Quantization for LLMs
  ACL Findings 2024 [Paper] [Code]

• AWEQ: Post-Training Quantization with Activation-Weight Equalization for Large Language Models
  Arxiv 2023 [Paper]

• Atom: Low-bit Quantization for Efficient and Accurate LLM Serving
  MLSys 2024 [Paper] [Code]

• QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models
  Arxiv 2023 [Paper]

• Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models
  Arxiv 2023 [Paper]

• On the Impact of Calibration Data in Post-training Quantization and Pruning
  ACL 2024 [Paper]

• A Speed Odyssey for Deployable Quantization of LLMs
  Arxiv 2023 [Paper]

• Fast and Efficient 2-bit LLM Inference on GPU: 2/4/16-bit in a Weight Matrix with Asynchronous Dequantization
  Arxiv 2023 [Paper]

• Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing
  NeurIPS 2023 [Paper] [Code]

• Efficient LLM Inference on CPUs
  NeurIPS 2023 on Efficient Natural Language and Speech Processing [Paper] [Code]

• The Cost of Compression: Investigating the Impact of Compression on Parametric Knowledge in Language Models
  EMNLP Findings 2023 [Paper]

• Zero-Shot Sharpness-Aware Quantization for Pre-trained Language Models
  EMNLP 2023 [Paper]

• Revisiting Block-based Quantisation: What is Important for Sub-8-bit LLM Inference?
  EMNLP 2023 [Paper] [Code]

• Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling
  EMNLP 2023 [Paper]

• Watermarking LLMs with Weight Quantization
  EMNLP 2023 [Paper] [Code]

• Enhancing Computation Efficiency in Large Language Models through Weight and Activation Quantization
  EMNLP 2023 [Paper]

• LLM-FP4: 4-Bit Floating-Point Quantized Transformers
  EMNLP 2023 [Paper] [Code]

• Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge
  AAAI 2024 [Paper]

• SmoothQuant+: Accurate and Efficient 4-bit Post-Training WeightQuantization for LLM
  Arxiv 2023 [Paper]

• CBQ: Cross-Block Quantization for Large Language Models
  Arxiv 2023 [Paper]

• ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks
  Arxiv 2023 [Paper]

• QuIP: 2-Bit Quantization of Large Language Models With Guarantees
  NeurIPS 2023 [Paper] [Code]

• A Performance Evaluation of a Quantized Large Language Model on Various Smartphones
  Arxiv 2023 [Paper]

• DeltaZip: Efficient Serving of Multiple Full-Model-Tuned LLMs
  EuroSys 2025 [Paper] [Code]

2022  ·  6 papers

• ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
  NeurIPS 2022 [Paper] [Code (DeepSpeed)]

• LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
  NeurIPS 2022 [Paper] [Code]

• Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models
  NeurIPS 2022 [Paper] [Code]

• LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models
  ICLR 2024 [Paper]

• SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
  ICML 2023 [Paper] [Code]

• GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
  ICLR 2023 [Paper] [Code]

Pruning and Sparsity

2026  ·  33 papers

• Adaptive Layer Selection for Layer-Wise Token Pruning in LLM Inference
  ACL Findings 2026 [Paper] [Code]

• LLMs can Compress LLMs: Adaptive Pruning by Agents
  Arxiv 2026 [Paper]

• Streaming-dLLM: Accelerating Diffusion LLMs via Suffix Pruning and Dynamic Decoding
  Arxiv 2026 [Paper] [Code]

• GradPruner: Gradient-Guided Layer Pruning Enabling Efficient Fine-Tuning and Inference for LLMs
  ICLR 2026 [Paper]

• FASA: Frequency-aware Sparse Attention
  ICLR 2026 [Paper]

• Compressing LLMs with MoP: Mixture of Pruners
  Arxiv 2026 [Paper] [Code]

• Pruning as a Cooperative Game: Surrogate-Assisted Layer Contribution Estimation for Large Language Models
  ICLR 2026 [Paper]

• Sink-Aware Pruning for Diffusion Language Models
  Arxiv 2026 [Paper] [Code]

• Curvature-Weighted Capacity Allocation: A Minimum Description Length Framework for Layer-Adaptive Large Language Model Optimization
  UAI 2026 [Paper] [Code]

• Sparse-BitNet: 1.58-bit LLMs are Naturally Friendly to Semi-Structured Sparsity
  Arxiv 2026 [Paper] [Code]

• Stem: Rethinking Causal Information Flow in Sparse Attention
  ICML 2026 [Paper]

• High-Fidelity Pruning for Large Language Models
  Arxiv 2026 [Paper] [Code]

• Sparser, Faster, Lighter Transformer Language Models
  Arxiv 2026 [Paper] [Code]

• REAM: Merging Improves Pruning of Experts in LLMs
  Arxiv 2026 [Paper] [Code]

• GRASPrune: Global Gating for Budgeted Structured Pruning of Large Language Models
  ACL 2026 [Paper]

• Revisiting the Effectiveness of LLM Pruning for Test-Time Scaling
  EMNLP Findings 2026 [Paper]

• Compute Where it Counts: Self Optimizing Language Models
  Arxiv 2026 [Paper] [Code]

• Ghosted Layers: Unconstrained Activation Alignment for Recovering Layer-Pruned LLMs
  Arxiv 2026 [Paper] [Code]

• LEAP: Learnable End-to-End Adaptive Pruning of Large Language Models
  ICML 2026 Workshop [Paper]

• Efficient Long-Context Modeling in Diffusion Language Models via Block Approximate Sparse Attention
  CVPR 2026 [Paper]

• Locality-Aware Redundancy Pruning for LLM Depth Compression
  Arxiv 2026 [Paper] [Code]

• FlashMemory-DeepSeek-V4: Lightning Index Ultra-Long Context via Lookahead Sparse Attention
  Arxiv 2026 [Paper] [Code]

• Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy
  Arxiv 2026 [Paper] [Code]

• Persona-Pruner: Sculpting Lightweight Models for Role-Playing
  ICML 2026 [Paper] [Code]

• Dustin: Draft-Augmented Sparse Verification for Efficient Long-Context Generation with Speculative Decoding
  ICML 2026 [Paper]

• EPTS: Elastic Post-Training Sparsity for Efficient Large Language Model Compression
  KDD 2026 [Paper]

• Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning
  Arxiv 2026 [Paper] [Code]

• WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning
  Arxiv 2026 [Paper] [Code]

• Celty: SpMspV GPU Kernel and SIMT Co-Design for Efficient Dual-Sparse LLM Inference
  ICCAD 2026 [Paper] [Code]

• The Sparsity Whisperer
  Arxiv 2026 [Paper] [Code]

• Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models
  ECCV 2026 [Paper]

• Router Sensitivity Under Lightweight Fine-Tuning Identifies Prunable Experts in Mixture-of-Experts Models
  Arxiv 2026 [Paper] [Code]

• Reservoir of Importance: Learning Semi-Structured Sparsity with Differentiable Subset Sampling
  EMNLP 2026 [Paper]

2025  ·  60 papers

• FASP: Fast and Accurate Structured Pruning of Large Language Models
  Arxiv 2025 [Paper]

• MultiPruner: Balanced Structure Removal in Foundation Models
  Arxiv 2025 [Paper] [Code]

• Mamba-Shedder: Post-Transformer Compression for Efficient Selective Structured State Space Models
  NAACL 2025 [Paper] [Code]

• 2SSP: A Two-Stage Framework for Structured Pruning of LLMs
  Arxiv 2025 [Paper] [Code]

• You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning
  ICLR 2025 [Paper]

• SwiftPrune: Hessian-Free Weight Pruning for Large Language Models
  Arxiv 2025 [Paper]

• Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models
  ICML 2025 [Paper]

• Twilight: Adaptive Attention Sparsity with Hierarchical Top-p Pruning
  NeurIPS 2025 [Paper]

• Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training
  Arxiv 2025 [Paper]

• Dobi-SVD: Differentiable SVD for LLM Compression and Some New Perspectives
  ICLR 2025 [Paper] [Homepage]

• EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models
  Arxiv 2025 [Paper] [Code]

• DarwinLM: Evolutionary Structured Pruning of Large Language Models
  COLM 2026 [Paper]

• MaskPrune: Mask-based LLM Pruning for Layer-wise Uniform Structures
  Arxiv 2025 [Paper]

• Determining Layer-wise Sparsity for Large Language Models Through a Theoretical Perspective
  Arxiv 2025 [Paper]

• PPC-GPT: Federated Task-Specific Compression of Large Language Models via Pruning and Chain-of-Thought Distillation
  Arxiv 2025 [Paper]

• Compression Scaling Laws: Unifying Sparsity and Quantization
  Arxiv 2025 [Paper]

• PASER: Post-Training Data Selection for Efficient Pruned Large Language Model Recovery
  ICLR 2026 [Paper]

• Týr-the-Pruner: Unlocking Accurate 50% Structural Pruning for LLMs via Global Sparsity Distribution Optimization
  Arxiv 2025 [Paper]

• Thanos: A Block-wise Pruning Algorithm for Efficient Large Language Model Compression
  Arxiv 2025 [Paper]

• Efficient LLMs with AMP: Attention Heads and MLP Pruning
  IJCNN 2025 [Paper]

• ReplaceMe: Network Simplification via Layer Pruning and Linear Transformations
  NeurIPS 2025 [Paper] [Code]

• Large Language Model Compression with Global Rank and Sparsity Optimization
  Arxiv 2025 [Paper]

• TRIM: Achieving Extreme Sparsity with Targeted Row-wise Iterative Metric-driven Pruning
  Arxiv 2025 [Paper] [Code]

• RAP: Runtime-Adaptive Pruning for LLM Inference
  Arxiv 2025 [Paper]

• Two-Stage Regularization-Based Structured Pruning for LLMs
  ACL 2026 [Paper]

• Pangu Light: Weight Re-Initialization for Pruning and Accelerating LLMs
  Arxiv 2025 [Paper]

• Sparsified State-Space Models are Efficient Highway Networks
  TMLR 2025 [Paper] [Code]

• SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models
  Arxiv 2025 [Paper] [Code]

• Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models
  Arxiv 2025 [Paper]

• Beyond Manually Designed Pruning Policies with Second-Level Performance Prediction: A Pruning Framework for LLMs
  Arxiv 2025 [Paper] [Code]

• Pruning Large Language Models by Identifying and Preserving Functional Networks
  Arxiv 2025 [Paper] [Code]

• SlimInfer: Accelerating Long-Context LLM Inference via Dynamic Token Pruning
  Arxiv 2025 [Paper] [Code]

• EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models
  Arxiv 2025 [Paper]

• Z-Pruner: Post-Training Pruning of Large Language Models for Efficiency without Retraining
  AICCSA 2025 [Paper] [Code]

• H2EAL: Hybrid-Bonding Architecture with Hybrid Sparse Attention for Efficient Long-Context LLM Inference
  ICCAD 2025 [Paper]

• Less Is More? Examining Fairness in Pruned Large Language Models for Summarising Opinions
  EMNLP 2025 [Paper] [Code]

• DualSparse-MoE: Coordinating Tensor/Neuron-Level Sparsity with Expert Partition and Reconstruction
  Arxiv 2025 [Paper]

• Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks
  ICLR 2026 [Paper] [Code]

• Spatio-Temporal Pruning for Compressed Spiking Large Language Models
  Arxiv 2025 [Paper]

• Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs
  EMNLP 2025 [Paper]

• Reasoning Models Can be Accurately Pruned Via Chain-of-Thought Reconstruction
  Arxiv 2025 [Paper] [Code]

• NIRVANA: Structured Pruning Reimagined for Large Language Model Compression
  COLM 2026 [Paper]

• HEAPr: Hessian-based Efficient Atomic Expert Pruning in Output Space
  ICLR 2026 [Paper] [Code]

• ProxyAttn: Guided Sparse Attention via Representative Heads
  ICLR 2026 [Paper]

• Effective Model Pruning: Measure The Redundancy of Model Components
  ICML 2026 [Paper]

• The Unseen Frontier: Pushing the Limits of LLM Sparsity with Surrogate-Free ADMM
  ICLR 2026 [Paper]

• ARMOR: High-Performance Semi-Structured Pruning via Adaptive Matrix Factorization
  ICLR 2026 [Paper] [Code]

• RCPU: Rotation-Constrained Error Compensation for Structured Pruning of Large Language Models
  ICLR 2026 [Paper]

• Fewer Weights, More Problems: A Practical Attack on LLM Pruning
  ICLR 2026 [Paper] [Code]

• From Local to Global: Revisiting Structured Pruning Paradigms for Large Language Models
  ACL 2026 [Paper]

• Sparser Block-Sparse Attention via Token Permutation
  ICML 2026 [Paper] [Code]

• Restoring Pruned Large Language Models via Lost Component Compensation
  NeurIPS 2025 [Paper]

• When Fewer Layers Break More Chains: Layer Pruning Harms Test-Time Scaling in LLMs
  Arxiv 2025 [Paper] [Code]

• 1+1>2: A Synergistic Sparse and Low-Rank Compression Method for Large Language Models
  EMNLP Findings 2025 [Paper]

• SpecAttn: Speculating Sparse Attention
  NeurIPS 2025 Workshop [Paper]

• IG-Pruning: Input-Guided Block Pruning for Large Language Models
  EMNLP 2025 [Paper] [Code]

• MACKO: Sparse Matrix-Vector Multiplication for Low Sparsity
  Arxiv 2025 [Paper] [Code]

• Understanding and Harnessing Sparsity in Unified Multimodal Models
  Arxiv 2025 [Paper] [Code]

• Resting Neurons, Active Insights: Robustifying Activation Sparsity in LLMs via Spontaneity
  ICML 2026 [Paper]

• Fragile Knowledge, Robust Instruction-Following: The Width Pruning Dichotomy in Llama-3.2
  Arxiv 2025 [Paper] [Code]

2024  ·  77 papers

• Plug-and-Play: An Efficient Post-training Pruning Method for Large Language Models
  ICLR 2024 [Paper] [Code]

• Fast and Optimal Weight Update for Pruned Large Language Models
  Arxiv 2024 [Paper]

• APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference
  ICML 2024 [Paper]

• Scaling Sparse Fine-Tuning to Large Language Models
  Arxiv 2024 [Paper]

• SliceGPT: Compress Large Language Models by Deleting Rows and Columns
  ICLR 2024 [Paper] [Code]

• Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods
  ICLR 2024 Workshop [Paper]

• Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes
  Arxiv 2024 [Paper] [Code]

• NutePrune: Efficient Progressive Pruning with Numerous Teachers for Large Language Models
  Arxiv 2024 [Paper]

• LaCo: Large Language Model Pruning via Layer Collapse
  EMNLP Findings 2024 [Paper]

• Why Lift so Heavy? Slimming Large Language Models by Cutting Off the Layers
  Arxiv 2024 [Paper]

• EBFT: Effective and Block-Wise Fine-Tuning for Sparse LLMs
  Arxiv 2024 [Paper] [Code]

• Data-free Weight Compress and Denoise for Large Language Models
  Arxiv 2024 [Paper]

• Gradient-Free Adaptive Global Pruning for Pre-trained Language Models
  NeurIPS 2024 [Paper]

• ShortGPT: Layers in Large Language Models are More Redundant Than You Expect
  Arxiv 2024 [Paper]

• LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models
  ICCV 2025 [Paper] [Code]

• Streamlining Redundant Layers to Compress Large Language Models
  Arxiv 2024 [Paper]

• LoRAP: Transformer Sub-Layers Deserve Differentiated Structured Compression for Large Language Models
  Arxiv 2024 [Paper]

• LoNAS: Elastic Low-Rank Adapters for Efficient Large Language Models
  COLING 2024 [Paper] [Code]

• Shears: Unstructured Sparsity with Neural Low-rank Adapter Search
  NAACL 2024 [Paper] [Code]

• Eigenpruning: an Interpretability-Inspired PEFT Method
  NAACL 2024 Abstract [Paper]

• OpenBA-V2: Reaching 77.3% High Compression Ratio with Fast Multi-Stage Pruning
  Arxiv 2024 [Paper]

• Pruning as a Domain-specific LLM Extractor
  NAACL 2024 Findings [Paper] [Code]

• Differentiable Model Scaling using Differentiable Topk
  ICML 2024 [Paper]

• COPAL: Continual Pruning in Large Language Generative Models
  ICML 2024 [Paper]

• Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models
  ICML 2024 [Paper] [[Code]](https://github.com/pprp/P