Image de couverture de livre

Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulation

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Kim, Joon Ha, Kim, Geon-Woo, Rachakonda, Anoop, Kim, Daehyeok
Format:	Preprint
Publié:	2026
Sujets:	Distributed, Parallel, and Cluster Computing Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Vulcan: Instance-Optimal Systems Heuristics Through LLM-Driven Search
par: Dwivedula, Rohit, et autres
Publié: (2025)

LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at Scale
par: Cho, Jaehong, et autres
Publié: (2024)

Large Language Models as Realistic Microservice Trace Generators
par: Kim, Donghyun, et autres
Publié: (2024)

OMEGA: A Low-Latency GNN Serving System for Large Graphs
par: Kim, Geon-Woo, et autres
Publié: (2025)

Why Should the Server Do It All?: A Scalable, Versatile, and Model-Agnostic Framework for Server-Light DNN Inference over Massively Distributed Clients via Training-Free Intermediate Feature Compression
par: Sung, Mingyu, et autres
Publié: (2025)

FairBatching: Fairness-Aware Batch Formation for LLM Inference
par: Lyu, Hongtao, et autres
Publié: (2025)

Token-Budget-Aware Pool Routing for Cost-Efficient LLM Inference
par: Chen, Huamin, et autres
Publié: (2026)

TAPAS: Thermal- and Power-Aware Scheduling for LLM Inference in Cloud Platforms
par: Stojkovic, Jovan, et autres
Publié: (2025)

MoSKA: Mixture of Shared KV Attention for Efficient Long-Sequence LLM Inference
par: Rhee, Myunghyun, et autres
Publié: (2025)

Watt Counts: Energy-Aware Benchmark for Sustainable LLM Inference on Heterogeneous GPU Architectures
par: Argerich, Mauricio Fadel, et autres
Publié: (2026)

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference
par: Jeong, Bodon, et autres
Publié: (2026)

Profiling-Driven Adaptive Distributed Transformer Inference on Embedded Edge Deployment
par: Qazi, Muhammad Azlan, et autres
Publié: (2026)

Accelerating LLM Inference with Precomputed Query Storage
par: Park, Jay H., et autres
Publié: (2025)

LLM Inference Serving: Survey of Recent Advances and Opportunities
par: Li, Baolin, et autres
Publié: (2024)

Decentralized AI: Permissionless LLM Inference on POKT Network
par: Olshansky, Daniel, et autres
Publié: (2024)

Federated Attention: A Distributed Paradigm for Collaborative LLM Inference over Edge Networks
par: Deng, Xiumei, et autres
Publié: (2025)

Taming the Chaos: Coordinated Autoscaling for Heterogeneous and Disaggregated LLM Inference
par: Li, Rongzhi, et autres
Publié: (2025)

KAIROS: Stateful, Context-Aware Power-Efficient Agentic Inference Serving
par: Yuan, Yichao, et autres
Publié: (2026)

Identifying and Mitigating Systemic Measurement Bias in Production LLM Inference Benchmarks
par: Chandrasekar, Ashok, et autres
Publié: (2026)

Seesaw: High-throughput LLM Inference via Model Re-sharding
par: Su, Qidong, et autres
Publié: (2025)

DeServe: Towards Affordable Offline LLM Inference via Decentralization
par: Wu, Linyu, et autres
Publié: (2025)

Frontier: Towards Comprehensive and Accurate LLM Inference Simulation
par: Feng, Yicheng, et autres
Publié: (2026)

Frontier: Simulating the Next Generation of LLM Inference Systems
par: Feng, Yicheng, et autres
Publié: (2025)

AIConfigurator: Lightning-Fast Configuration Optimization for Multi-Framework LLM Serving
par: Xu, Tianhao, et autres
Publié: (2026)

SPECTRE: Hybrid Ordinary-Parallel Speculative Serving for Resource-Efficient LLM Inference
par: Xie, Jincheng, et autres
Publié: (2026)

Speculative Decoding in Decentralized LLM Inference: Turning Communication Latency into Computation Throughput
par: Song, Jingwei, et autres
Publié: (2025)

Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference Workloads
par: Wilkins, Grant, et autres
Publié: (2024)

FlowSpec: Continuous Pipelined Speculative Decoding for Efficient Distributed LLM Inference
par: Liu, Xing, et autres
Publié: (2025)

LAPS: A Length-Aware-Prefill LLM Serving System
par: She, Jianshu, et autres
Publié: (2026)

PALS: Power-Aware LLM Serving for Mixture-of-Experts Models
par: Hankendi, Can, et autres
Publié: (2026)

BlockLLM: Multi-tenant Finer-grained Serving for Large Language Models
par: Hu, Bodun, et autres
Publié: (2024)

Toward Cost-Efficient Serving of Mixture-of-Experts with Asynchrony
par: Wang, Shaoyu, et autres
Publié: (2025)

Why Do AI Agents Systematically Fail at Cloud Root Cause Analysis?
par: Kim, Taeyoon, et autres
Publié: (2026)

DWDP: Distributed Weight Data Parallelism for High-Performance LLM Inference on NVL72
par: Li, Wanqian, et autres
Publié: (2026)

BucketServe: Bucket-Based Dynamic Batching for Smart and Efficient LLM Inference Serving
par: Zheng, Wanyi, et autres
Publié: (2025)

Multi-IaC-Eval: Benchmarking Cloud Infrastructure as Code Across Multiple Formats
par: Davidson, Sam, et autres
Publié: (2025)

TinyServe: Query-Aware Cache Selection for Efficient LLM Serving
par: Liu, Dong, et autres
Publié: (2025)

ExeGPT: Constraint-Aware Resource Scheduling for LLM Inference
par: Oh, Hyungjun, et autres
Publié: (2024)

Compare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data
par: Son, Ha Min, et autres
Publié: (2021)

Striking the Right Balance between Compute and Copy: Improving LLM Inferencing Under Speculative Decoding
par: Ramachandran, Arun, et autres
Publié: (2025)