Copertina

Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Brüel-Gabrielsson, Rickard, Zhu, Jiacheng, Bhardwaj, Onkar, Choshen, Leshem, Greenewald, Kristjan, Yurochkin, Mikhail, Solomon, Justin
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Distributed, Parallel, and Cluster Computing Artificial Intelligence Computation and Language Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

S-LoRA: Serving Thousands of Concurrent LoRA Adapters
di: Sheng, Ying, et al.
Pubblicazione: (2023)

Efficient Multi-Adapter LLM Serving via Cross-Model KV-Cache Reuse with Activated LoRA
di: Li, Allison, et al.
Pubblicazione: (2025)

Asymmetry in Low-Rank Adapters of Foundation Models
di: Zhu, Jiacheng, et al.
Pubblicazione: (2024)

Serving Heterogeneous LoRA Adapters in Distributed LLM Inference Systems
di: Jaiswal, Shashwat, et al.
Pubblicazione: (2025)

InfiniLoRA: Disaggregated Multi-LoRA Serving for Large Language Models
di: Chen, Hongyu, et al.
Pubblicazione: (2026)

CaraServe: CPU-Assisted and Rank-Aware LoRA Serving for Generative LLM Inference
di: Li, Suyi, et al.
Pubblicazione: (2024)

Slicing Mutual Information Generalization Bounds for Neural Networks
di: Nadjahi, Kimia, et al.
Pubblicazione: (2024)

ForkKV: Scaling Multi-LoRA Agent Serving via Copy-on-Write Disaggregated KV Cache
di: Wang, Shao, et al.
Pubblicazione: (2026)

Deep Augmentation: Dropout as Augmentation for Self-Supervised Learning
di: Brüel-Gabrielsson, Rickard, et al.
Pubblicazione: (2023)

SHE-LoRA: Selective Homomorphic Encryption for Federated Tuning with Heterogeneous LoRA
di: Liu, Jianmin, et al.
Pubblicazione: (2025)

Federated LoRA with Sparse Communication
di: Kuo, Kevin, et al.
Pubblicazione: (2024)

ALTO: Adaptive LoRA Tuning and Orchestration for Heterogeneous LoRA Training Workloads
di: Zuo, Jingwei, et al.
Pubblicazione: (2026)

ExpertWeave: Efficiently Serving Expert-Specialized Fine-Tuned Adapters at Scale
di: Shi, Ge, et al.
Pubblicazione: (2025)

SkyServe: Serving AI Models across Regions and Clouds with Spot Instances
di: Mao, Ziming, et al.
Pubblicazione: (2024)

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices
di: Shen, Zheyu, et al.
Pubblicazione: (2025)

pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning
di: Yi, Liping, et al.
Pubblicazione: (2023)

ServerlessLoRA: Minimizing Latency and Cost in Serverless Inference for LoRA-Based LLMs
di: Sui, Yifan, et al.
Pubblicazione: (2025)

FDLoRA: Personalized Federated Learning of Large Language Model via Dual LoRA Tuning
di: QI, Jiaxing, et al.
Pubblicazione: (2024)

DeepServe: Serverless Large Language Model Serving at Scale
di: Hu, Junhao, et al.
Pubblicazione: (2025)

EdgeServing: Deadline-Aware Multi-DNN Serving at the Edge
di: Cao, Jiahe, et al.
Pubblicazione: (2026)

Data Driven Optimization of GPU efficiency for Distributed LLM Adapter Serving
di: Agullo, Ferran, et al.
Pubblicazione: (2026)

DynaServe: Unified and Elastic Execution for Dynamic Disaggregated LLM Serving
di: Ruan, Chaoyi, et al.
Pubblicazione: (2025)

MuxServe: Flexible Spatial-Temporal Multiplexing for Multiple LLM Serving
di: Duan, Jiangfei, et al.
Pubblicazione: (2024)

TridentServe: A Stage-level Serving System for Diffusion Pipelines
di: Xia, Yifei, et al.
Pubblicazione: (2025)

Can LoRA Fusion Support Cross-Domain Tasks in Cloud-Edge Collaboration?
di: Wang, Yatong, et al.
Pubblicazione: (2026)

LoRAFusion: Efficient LoRA Fine-Tuning for LLMs
di: Zhu, Zhanda, et al.
Pubblicazione: (2025)

Predictive-LoRA: A Proactive and Fragmentation-Aware Serverless Inference System for LLMs
di: Ni, Yinan, et al.
Pubblicazione: (2025)

LoRA-C: Parameter-Efficient Fine-Tuning of Robust CNN for IoT Devices
di: Ding, Chuntao, et al.
Pubblicazione: (2024)

MemServe: Context Caching for Disaggregated LLM Serving with Elastic Memory Pool
di: Hu, Cunchen, et al.
Pubblicazione: (2024)

EconoServe: Maximizing Multi-Resource Utilization with SLO Guarantees in LLM Serving
di: Shen, Haiying, et al.
Pubblicazione: (2024)

ThunderServe: High-performance and Cost-efficient LLM Serving in Cloud Environments
di: Jiang, Youhe, et al.
Pubblicazione: (2025)

ServeGen: Workload Characterization and Generation of Large Language Model Serving in Production
di: Xiang, Yuxing, et al.
Pubblicazione: (2025)

HydraServe: Minimizing Cold Start Latency for Serverless LLM Serving in Public Clouds
di: Lou, Chiheng, et al.
Pubblicazione: (2025)

DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving
di: Zhong, Yinmin, et al.
Pubblicazione: (2024)

SparseServe: Unlocking Parallelism for Dynamic Sparse Attention in Long-Context LLM Serving
di: Zhou, Qihui, et al.
Pubblicazione: (2025)

Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models
di: Cho, Yae Jee, et al.
Pubblicazione: (2024)

CascadeServe: Unlocking Model Cascades for Inference Serving
di: Kossmann, Ferdi, et al.
Pubblicazione: (2024)

PolyServe: Efficient Multi-SLO Serving at Scale
di: Zhu, Kan, et al.
Pubblicazione: (2025)

SLOs-Serve: Optimized Serving of Multi-SLO LLMs
di: Chen, Siyuan, et al.
Pubblicazione: (2025)

Improving LoRA in Privacy-preserving Federated Learning
di: Sun, Youbang, et al.
Pubblicazione: (2024)