Cover Image

EXAQ: Exponent Aware Quantization For LLMs Acceleration

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shkolnik, Moran, Fishman, Maxim, Chmiel, Brian, Ben-Yaacov, Hilla, Banner, Ron, Levy, Kfir Yehuda
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence Performance
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FP4 All the Way: Fully Quantized Training of LLMs
by: Chmiel, Brian, et al.
Published: (2025)

Scaling FP8 training to trillion-token LLMs
by: Fishman, Maxim, et al.
Published: (2024)

Normalized Architectures are Natively 4-Bit
by: Fishman, Maxim, et al.
Published: (2026)

Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats
by: Chmiel, Brian, et al.
Published: (2021)

Minimum Variance Unbiased N:M Sparsity for the Neural Gradients
by: Chmiel, Brian, et al.
Published: (2022)

LLMs for Analog Circuit Design Continuum (ACDC)
by: Esfandiari, Yasaman, et al.
Published: (2025)

ALISA: Accelerating Large Language Model Inference via Sparsity-Aware KV Caching
by: Zhao, Youpeng, et al.
Published: (2024)

Dynamic Expert Quantization for Scalable Mixture-of-Experts Inference
by: Chu, Kexin, et al.
Published: (2025)

Accelerating Diffusion LLMs via Adaptive Parallel Decoding
by: Israel, Daniel, et al.
Published: (2025)

OSCAR: Offline Spectral Covariance-Aware Rotation for 2-bit KV Cache Quantization
by: Zhou, Zhongzhu, et al.
Published: (2026)

SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression
by: Mozaffari, Mohammad, et al.
Published: (2024)

SparAMX: Accelerating Compressed LLMs Token Generation on AMX-powered CPUs
by: AbouElhamayed, Ahmed F., et al.
Published: (2025)

Unveiling the Potential of Quantization with MXFP4: Strategies for Quantization Error Reduction
by: Chhugani, Jatin, et al.
Published: (2026)

PATCH: Learnable Tile-level Hybrid Sparsity for LLMs
by: Hourri, Younes, et al.
Published: (2025)

Energy-Aware LLMs: A step towards sustainable AI for downstream applications
by: Tran, Nguyen Phuc, et al.
Published: (2025)

GreedySnake: Accelerating SSD-Offloaded LLM Training with Efficient Scheduling and Optimizer Step Overlapping
by: Yin, Yishu, et al.
Published: (2025)

QJL: 1-Bit Quantized JL Transform for KV Cache Quantization with Zero Overhead
by: Zandieh, Amir, et al.
Published: (2024)

Ghosted Layers: Unconstrained Activation Alignment for Recovering Layer-Pruned LLMs
by: Yun, Vincent-Daniel, et al.
Published: (2026)

OPTIMA: Optimal One-shot Pruning for LLMs via Quadratic Programming Reconstruction
by: Mozaffari, Mohammad, et al.
Published: (2025)

MoEITS: A Green AI approach for simplifying MoE-LLMs
by: Balderas, Luis, et al.
Published: (2026)

Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models
by: Chiang, Hung-Yueh, et al.
Published: (2025)

Accelerating AI Performance using Anderson Extrapolation on GPUs
by: Dajani, Saleem Abdul Fattah Ahmed Al, et al.
Published: (2024)

QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving
by: Lin, Yujun, et al.
Published: (2024)

Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey
by: Du, Dayou, et al.
Published: (2024)

REAM: Merging Improves Pruning of Experts in LLMs
by: Jha, Saurav, et al.
Published: (2026)

Automatic Generation of Fast and Accurate Performance Models for Deep Neural Network Accelerators
by: Lübeck, Konstantin, et al.
Published: (2024)

It's all about PR -- Smart Benchmarking AI Accelerators using Performance Representatives
by: Jung, Alexander Louis-Ferdinand, et al.
Published: (2024)

FlexiSAGA: A Flexible Systolic Array GEMM Accelerator for Sparse and Dense Processing
by: Müller, Mika Markus, et al.
Published: (2025)

KernelBench: Can LLMs Write Efficient GPU Kernels?
by: Ouyang, Anne, et al.
Published: (2025)

A Performance Evaluation of a Quantized Large Language Model on Various Smartphones
by: Çöplü, Tolga, et al.
Published: (2023)

Unleashing Automated Congestion Control Customization in the Wild
by: Cohen, Amit, et al.
Published: (2025)

RooflineBench: A Benchmarking Framework for On-Device LLMs via Roofline Analysis
by: Bi, Zhen, et al.
Published: (2026)

Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement
by: Rajput, Saurabhsingh, et al.
Published: (2023)

Kevin: Multi-Turn RL for Generating CUDA Kernels
by: Baronio, Carlo, et al.
Published: (2025)

FlashSVD: Memory-Efficient Inference with Streaming for Low-Rank Models
by: Shao, Zishan, et al.
Published: (2025)

Reducing Latency of LLM Search Agent via Speculation-based Algorithm-System Co-Design
by: Huang, Zixiao, et al.
Published: (2025)

Quantum Neural Networks for Wind Energy Forecasting: A Comparative Study of Performance and Scalability with Classical Models
by: Hangun, Batuhan, et al.
Published: (2025)

Deploying Open-Source Large Language Models: A performance Analysis
by: Bendi-Ouis, Yannis, et al.
Published: (2024)

Ragged Paged Attention: A High-Performance and Flexible LLM Inference Kernel for TPU
by: Jiang, Jevin, et al.
Published: (2026)

Generalizing Scaling Laws for Dense and Sparse Large Language Models
by: Hossain, Md Arafat, et al.
Published: (2025)