Cover Image

Generalizing Scaling Laws for Dense and Sparse Large Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hossain, Md Arafat, Wu, Xingfu, Taylor, Valerie, Jannesari, Ali
Format:	Preprint
Published:	2025
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

The Race to Efficiency: A New Perspective on AI Scaling Laws
by: Lu, Chien-Ping
Published: (2025)

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

Fairness in Serving Large Language Models
by: Sheng, Ying, et al.
Published: (2023)

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

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

Research on Low-Latency Inference and Training Efficiency Optimization for Graph Neural Network and Large Language Model-Based Recommendation Systems
by: Zhao, Yushang, et al.
Published: (2025)

FlashOmni: A Unified Sparse Attention Engine for Diffusion Transformers
by: Qiao, Liang, et al.
Published: (2025)

Throughput Optimization as a Strategic Lever in Large-Scale AI Systems: Evidence from Dataloader and Memory Profiling Innovations
by: Jha, Mayank
Published: (2026)

Comment on paper: Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problems
by: Min, Yimeng
Published: (2024)

An Efficient Hybrid Sparse Attention with CPU-GPU Parallelism for Long-Context Inference
by: Yao, Feiyu, et al.
Published: (2026)

Performance Modeling of Data Storage Systems using Generative Models
by: Al-Maeeni, Abdalaziz Rashid, et al.
Published: (2023)

Model Compression and Efficient Inference for Large Language Models: A Survey
by: Wang, Wenxiao, et al.
Published: (2024)

OMPILOT: Harnessing Transformer Models for Auto Parallelization to Shared Memory Computing Paradigms
by: Bhattacharjee, Arijit, et al.
Published: (2025)

Quantifying the Generalization Gap in Seizure Detection: A Large-Scale Empirical Benchmark via the SzCORE Challenge
by: Dan, Jonathan, et al.
Published: (2025)

On the Sustainability of AI Inferences in the Edge
by: Sobhani, Ghazal, et al.
Published: (2025)

An Autotuning-based Optimization Framework for Mixed-kernel SVM Classifications in Smart Pixel Datasets and Heterojunction Transistors
by: Wu, Xingfu, et al.
Published: (2024)

CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing
by: Zheng, Wenhao, et al.
Published: (2025)

Enhancing Inference Efficiency of Large Language Models: Investigating Optimization Strategies and Architectural Innovations
by: Tyukin, Georgy
Published: (2024)

Rudder: Steering Prefetching in Distributed GNN Training using LLM Agents
by: Sarkar, Aishwarya, et al.
Published: (2026)

Data Efficacy for Language Model Training
by: Dai, Yalun, et al.
Published: (2025)

Towards Generalized Parameter Tuning in Coherent Ising Machines: A Portfolio-Based Approach
by: Hanyu, Tatsuro, et al.
Published: (2025)

Improving the Serving Performance of Multi-LoRA Large Language Models via Efficient LoRA and KV Cache Management
by: Zhang, Hang, et al.
Published: (2025)

Leveraging Speculative Sampling and KV-Cache Optimizations Together for Generative AI using OpenVINO
by: Barad, Haim, et al.
Published: (2023)

LLM Swiss Round: Aggregating Multi-Benchmark Performance via Competitive Swiss-System Dynamics
by: Liu, Jiashuo, et al.
Published: (2025)

Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems
by: Miao, Xupeng, et al.
Published: (2023)

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

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

Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study
by: Siniosoglou, Ilias, et al.
Published: (2024)

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments
by: Almurshed, Osama, 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)

Stochastic Q-learning for Large Discrete Action Spaces
by: Fourati, Fares, et al.
Published: (2024)

FlashSVD v1.5: Making Low-Rank Transformers Inference Actually Fast
by: Wu, Wenhao, et al.
Published: (2026)

Democratizing AI: A Comparative Study in Deep Learning Efficiency and Future Trends in Computational Processing
by: Amin, Lisan Al, et al.
Published: (2026)

STAlloc: Enhancing Memory Efficiency in Large-Scale Model Training with Spatio-Temporal Planning
by: Huang, Zixiao, et al.
Published: (2025)

CodeRosetta: Pushing the Boundaries of Unsupervised Code Translation for Parallel Programming
by: TehraniJamsaz, Ali, et al.
Published: (2024)

MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations
by: Dutta, Akash, et al.
Published: (2024)

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

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

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

Energy-Efficient Transformer Inference: Optimization Strategies for Time Series Classification
by: Kermani, Arshia, et al.
Published: (2025)