FlexInfer: Breaking Memory Constraint via Flexible and Efficient Offloading for On-Device LLM Inference

• URL: http://arxiv.org/abs/2503.03777v1
• Date: Tue, 04 Mar 2025 20:08:03 GMT
• Title: FlexInfer: Breaking Memory Constraint via Flexible and Efficient Offloading for On-Device LLM Inference
• Authors: Hongchao Du, Shangyu Wu, Arina Kharlamova, Nan Guan, Chun Jason Xue,
• Abstract summary: Large Language Models (LLMs) face challenges for on-device inference due to high memory demands.<n>We propose FlexInfer, an optimized offloading framework for on-device inference.
• Score: 10.755373001278402
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) face challenges for on-device inference due to high memory demands. Traditional methods to reduce memory usage often compromise performance and lack adaptability. We propose FlexInfer, an optimized offloading framework for on-device inference, addressing these issues with techniques like asynchronous prefetching, balanced memory locking, and flexible tensor preservation. These strategies enhance memory efficiency and mitigate I/O bottlenecks, ensuring high performance within user-specified resource constraints. Experiments demonstrate that FlexInfer significantly improves throughput under limited resources, achieving up to 12.5 times better performance than existing methods and facilitating the deployment of large models on resource-constrained devices.

Related papers

• MemRec: Collaborative Memory-Augmented Agentic Recommender System [57.548438733740504]
  We propose MemRec, a framework that architecturally decouples reasoning from memory management.<n>MemRec introduces a dedicated LM_Mem to manage a dynamic collaborative memory graph.<n>It achieves state-of-the-art performance on four benchmarks.
  arXiv  Detail & Related papers  (2026-01-13T18:51:16Z)
• Mixture-of-Channels: Exploiting Sparse FFNs for Efficient LLMs Pre-Training and Inference [16.71963410333802]
  Large language models (LLMs) have demonstrated remarkable success across diverse artificial intelligence tasks.<n>MoC substantially reduces activation memory during pre-training.<n>MoC delivers significant memory savings and throughput gains while maintaining competitive model performance.
  arXiv  Detail & Related papers  (2025-11-12T13:30:57Z)
• ExpertFlow: Adaptive Expert Scheduling and Memory Coordination for Efficient MoE Inference [8.296993547783808]
  ExpertFlow is a runtime system for MoE inference that combines adaptive expert prefetching and cache-aware routing.<n>Our evaluation demonstrates that ExpertFlow reduces model stall time to less than 0.1% of the baseline.
  arXiv  Detail & Related papers  (2025-10-30T17:29:27Z)
• DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training [41.09085549544767]
  We introduce a Dynamic Activation Framework (DAF) that enables scalable and efficient on-device training through system-level optimizations.<n>DAF achieves both memory- and time-efficient dynamic quantization training by addressing key system bottlenecks.<n> Evaluations on various deep learning models across embedded and mobile platforms demonstrate up to a $22.9times$ reduction in memory usage and a $3.2times$ speedup.
  arXiv  Detail & Related papers  (2025-07-09T08:59:30Z)
• FlexQuant: A Flexible and Efficient Dynamic Precision Switching Framework for LLM Quantization [18.041828697950812]
  We propose FlexQuant, a dynamic precision-switching framework to optimize the trade-off between inference speed and accuracy.<n>Our work provides a comprehensive analysis of quantization strategies, introduces a precision requirement model for optimal switching, and implements efficient fine-grained precision management.<n> Experimental results demonstrate that FlexQuant achieves a 1.3x end-to-end speedup across diverse language tasks with negligible accuracy loss.
  arXiv  Detail & Related papers  (2025-05-21T07:42:53Z)
• Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning [39.73152182572741]
  This paper proposes a novel framework, named Quantized Split Federated Fine-Tuning Large AI Model (SFLAM) By partitioning the training load between edge devices and servers, SFLAM can facilitate the operation of large models on devices. SFLAM incorporates quantization management, power control, and bandwidth allocation strategies to enhance training efficiency.
  arXiv  Detail & Related papers  (2025-04-12T07:55:11Z)
• COSMOS: A Hybrid Adaptive Optimizer for Memory-Efficient Training of LLMs [81.01082659623552]
  Large Language Models (LLMs) have demonstrated remarkable success across various domains.<n>Their optimization remains a significant challenge due to the complex and high-dimensional loss landscapes they inhabit.
  arXiv  Detail & Related papers  (2025-02-24T18:42:19Z)
• Sparse Gradient Compression for Fine-Tuning Large Language Models [58.44973963468691]
  Fine-tuning large language models (LLMs) for downstream tasks has become increasingly crucial due to their widespread use and the growing availability of open-source models.<n>High memory costs associated with fine-tuning remain a significant challenge, especially as models increase in size.<n>We propose sparse compression gradient (SGC) to address these limitations.
  arXiv  Detail & Related papers  (2025-02-01T04:18:28Z)
• FlexQuant: Elastic Quantization Framework for Locally Hosted LLM on Edge Devices [3.950064543723201]
  Memory elasticity is crucial for edge devices with unified memory, where memory is shared and fluctuates dynamically.<n>We propose FlexQuant, a novel elasticity framework that generates an ensemble of quantized models.
  arXiv  Detail & Related papers  (2025-01-13T08:58:00Z)
• GUIDE: A Global Unified Inference Engine for Deploying Large Language Models in Heterogeneous Environments [1.0558515062670693]
  Large language models (LLMs) in real-world scenarios remains a critical challenge.<n>These challenges often lead to inefficiencies in memory utilization, latency, and throughput.<n>We develop a framework to address these issues, achieving prediction errors between 9.9% and 42.3% for key metrics such as batch latency, TTFT, and decode throughput.
  arXiv  Detail & Related papers  (2024-12-06T05:46:43Z)
• FluidML: Fast and Memory Efficient Inference Optimization [3.7676096626244986]
  We present FluidML, a generic runtime memory management and optimization framework. We show that FluidML can consistently reduce the end-to-end inference latency by up to 25.38% for popular language models. We also show that FluidML can reduce peak memory usage by up to 41.47%, compared to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-11-14T07:16:23Z)
• SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning [63.93193829913252]
  We propose an innovative METL strategy called SHERL for resource-limited scenarios. In the early route, intermediate outputs are consolidated via an anti-redundancy operation. In the late route, utilizing minimal late pre-trained layers could alleviate the peak demand on memory overhead.
  arXiv  Detail & Related papers  (2024-07-10T10:22:35Z)
• FedMef: Towards Memory-efficient Federated Dynamic Pruning [42.07105095641134]
  Federated learning (FL) promotes decentralized training while prioritizing data confidentiality. Its application on resource-constrained devices is challenging due to the high demand for computation and memory resources to train deep learning models. We propose FedMef, a novel and memory-efficient federated dynamic pruning framework.
  arXiv  Detail & Related papers  (2024-03-21T13:54:36Z)
• Contractive error feedback for gradient compression [60.05809370598166]
  We propose a communication efficient method called contractive error feedback (ConEF) As opposed to SGD with error-feedback (EFSGD) that inefficiently manages memory, ConEF obtains the sweet spot of convergence and memory usage. We empirically validate ConEF on various learning tasks that include image classification, language modeling, and machine translation.
  arXiv  Detail & Related papers  (2023-12-13T21:54:21Z)
• Boosting Inference Efficiency: Unleashing the Power of Parameter-Shared Pre-trained Language Models [109.06052781040916]
  We introduce a technique to enhance the inference efficiency of parameter-shared language models. We also propose a simple pre-training technique that leads to fully or partially shared models. Results demonstrate the effectiveness of our methods on both autoregressive and autoencoding PLMs.
  arXiv  Detail & Related papers  (2023-10-19T15:13:58Z)
• Energy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures [68.91874045918112]
  adapter-ALBERT is an efficient model optimization for maximal data reuse across different tasks. We demonstrate the advantage of mapping the model to a heterogeneous on-chip memory architecture by performing simulations on a validated NLP edge accelerator.
  arXiv  Detail & Related papers  (2023-03-25T14:40:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.