QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration

• URL: http://arxiv.org/abs/2505.06481v1
• Date: Sat, 10 May 2025 00:46:04 GMT
• Title: QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration
• Authors: HamidReza Imani, Jiaxin Peng, Peiman Mohseni, Abdolah Amirany, Tarek El-Ghazawi,
• Abstract summary: A server with a single NVIDIA A100 GPU (80GB) using Mixtral-8x7B models demonstrate an 85% average reduction in turnaround time compared to NVIDIA's multi-instance GPU (MIG)<n> experiments on Google's Switch Transformer Base-8 model with up to four variants demonstrate the scalability and resilience of our approach in maintaining output quality compared to other model merging baselines, highlighting its effectiveness.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The deployment of mixture-of-experts (MoE) large language models (LLMs) presents significant challenges due to their high memory demands. These challenges become even more pronounced in multi-tenant environments, where shared resources must accommodate multiple models, limiting the effectiveness of conventional virtualization techniques. This paper addresses the problem of efficiently serving multiple fine-tuned MoE-LLMs on a single-GPU. We propose a serving system that employs \textit{similarity-based expert consolidation} to reduce the overall memory footprint by sharing similar experts across models. To ensure output quality, we introduce \textit{runtime partial reconfiguration}, dynamically replacing non-expert layers when processing requests from different models. As a result, our approach achieves a competitive output quality while maintaining throughput comparable to serving a single model while incurring a negligible increase in time-to-first-token (TTFT). Experiments on a server with a single NVIDIA A100 GPU (80GB) using Mixtral-8x7B models demonstrate an 85\% average reduction in turnaround time compared to NVIDIA's multi-instance GPU (MIG). Furthermore, experiments on Google's Switch Transformer Base-8 model with up to four variants demonstrate the scalability and resilience of our approach in maintaining output quality compared to other model merging baselines, highlighting its effectiveness.

Related papers

• MEL: Multi-level Ensemble Learning for Resource-Constrained Environments [1.59297928921015]
  We propose a new framework for resilient edge inference, Multi-Level Ensemble Learning (MEL)<n>MEL trains multiple lightweight backup models capable of operating collaboratively, refining each other when multiple servers are available, and independently under failures.<n> Empirical evaluations across vision, language, and audio datasets show that MEL provides performance comparable to original architectures.
  arXiv  Detail & Related papers  (2025-06-25T02:33:57Z)
• ADAMIX: Adaptive Mixed-Precision Delta-Compression with Quantization Error Optimization for Large Language Models [14.975251449732175]
  Large language models (LLMs) achieve impressive performance on various knowledge-intensive and complex reasoning tasks.<n>Recent works explore delta-compression approaches to quantize and compress the delta parameters between the customized LLM and the corresponding base model.<n>We propose ADAmix, an effective adaptive mixed-precision delta-compression framework.
  arXiv  Detail & Related papers  (2025-06-05T08:17:12Z)
• EfficientLLaVA:Generalizable Auto-Pruning for Large Vision-language Models [64.18350535770357]
  We propose an automatic pruning method for large vision-language models to enhance the efficiency of multimodal reasoning.<n>Our approach only leverages a small number of samples to search for the desired pruning policy.<n>We conduct extensive experiments on the ScienceQA, Vizwiz, MM-vet, and LLaVA-Bench datasets for the task of visual question answering.
  arXiv  Detail & Related papers  (2025-03-19T16:07:04Z)
• Why Train Everything? Tint a Single Layer for Multi-task Model Merging [17.496018757317824]
  Model merging integrates independently fine-tuned models into a single multi-task model, offering a flexible alternative to joint training.<n>Many existing model merging methods introduce additional task-specific components, increasing complexity and requiring extra modifications.<n>We propose Model Tinting, a lightweight yet highly effective approach that improves model merging by updating just a single layer.
  arXiv  Detail & Related papers  (2024-12-26T07:42:06Z)
• Layerwise Recurrent Router for Mixture-of-Experts [42.36093735411238]
  Mixture-of-Experts (MoE) architecture stands out for its ability to scale model size without significantly increasing training costs.<n>Current MoE models often display parameter inefficiency.<n>We introduce the Layerwise Recurrent Router for Mixture-of-Experts (RMoE)
  arXiv  Detail & Related papers  (2024-08-13T10:25:13Z)
• Mixture of Experts with Mixture of Precisions for Tuning Quality of Service [0.0]
  This paper presents an adaptive serving approach for the efficient deployment of MoE models. By dynamically determining the number of quantized experts, we offer a fine-grained range of configurations for tuning throughput and model quality. Results highlight the practical applicability of our approach in dynamic and accuracy-sensitive applications.
  arXiv  Detail & Related papers  (2024-07-19T15:42:49Z)
• EMR-Merging: Tuning-Free High-Performance Model Merging [55.03509900949149]
  We show that Elect, Mask & Rescale-Merging (EMR-Merging) shows outstanding performance compared to existing merging methods. EMR-Merging is tuning-free, thus requiring no data availability or any additional training while showing impressive performance.
  arXiv  Detail & Related papers  (2024-05-23T05:25:45Z)
• Shortcut-connected Expert Parallelism for Accelerating Mixture-of-Experts [4.629608387540524]
  ScMoE is a novel shortcut-connected MoE architecture integrated with an overlapping parallelization strategy.<n>Compared to the prevalent top-2 MoE baseline, ScMoE achieves speedups of 1.49 times in training and 1.82 times in inference.
  arXiv  Detail & Related papers  (2024-04-07T17:17:23Z)
• Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image Restoration [100.54419875604721]
  All-in-one image restoration tackles different types of degradations with a unified model instead of having task-specific, non-generic models for each degradation. We propose DyNet, a dynamic family of networks designed in an encoder-decoder style for all-in-one image restoration tasks. Our DyNet can seamlessly switch between its bulkier and lightweight variants, thereby offering flexibility for efficient model deployment.
  arXiv  Detail & Related papers  (2024-04-02T17:58:49Z)
• Merging Multi-Task Models via Weight-Ensembling Mixture of Experts [64.94129594112557]
  Merging Transformer-based models trained on different tasks into a single unified model can execute all the tasks concurrently. Previous methods, exemplified by task arithmetic, have been proven to be both effective and scalable. We propose to merge most of the parameters while upscaling the Transformer layers to a weight-ensembling mixture of experts (MoE) module.
  arXiv  Detail & Related papers  (2024-02-01T08:58:57Z)
• Exploiting Inter-Layer Expert Affinity for Accelerating Mixture-of-Experts Model Inference [3.217776693788795]
  We propose a lightweight optimization technique called ExFlow to largely accelerate the inference of pre-trained MoE models. By exploiting the inter-layer expert affinity, our solution can be directly applied to pre-trained MoE models without any fine-tuning or accuracy degradation. Our solution beats the cutting-edge MoE implementations with experts from 8 to 64, with up to 2.2x improvement in inference throughput.
  arXiv  Detail & Related papers  (2024-01-16T14:16:47Z)
• Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA [58.040931661693925]
  We propose a strategy that combines redundant recomputing and out-of-core methods. We achieve an average of 1.52x speedup in six different models over the state-of-the-art out-of-core methods. Our data parallel out-of-core solution can outperform complex hybrid model parallelism in training large models, e.g. Megatron-LM and Turning-NLG.
  arXiv  Detail & Related papers  (2020-08-26T07:24:34Z)
• Multiscale Deep Equilibrium Models [162.15362280927476]
  We propose a new class of implicit networks, the multiscale deep equilibrium model (MDEQ) An MDEQ directly solves for and backpropagates through the equilibrium points of multiple feature resolutions simultaneously. We illustrate the effectiveness of this approach on two large-scale vision tasks: ImageNet classification and semantic segmentation on high-resolution images from the Cityscapes dataset.
  arXiv  Detail & Related papers  (2020-06-15T18:07:44Z)

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.