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MoE-SpAc: Efficient MoE Inference Based on Speculative Activation Utility in Heterogeneous Edge Scenarios

arXiv:2603.09983v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models enable scalable performance but face severe memory constraints on edge devices. Existing offloading strategies struggle with I/O bottlenecks due to the dynamic, low-information nature of autoregressive expert activation. In this paper, we propose to repurpose Speculative Decoding (SD) not merely as a compute accelerator, but as an informative lookahead sensor for memory management, supported by our theoretical and empirical analyses. Hence, we introduce MoE-SpAc, an MoE inference framework that integrates a Speculative Utility Estimator to track expert demand, a Heterogeneous Workload Balancer to dynamically partition computation via online integer optimization, and an Asynchronous Execution Engine to unify the prefetching and eviction in the same utility space. Extensive experiments on seven benchmarks demonstrate that MoE-SpAc achieves a 42% improvement in TPS over the SOTA SD-based baseline, and an av

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Shuhuai Li, Jianghao Lin, Dongdong Ge, Yinyu Ye · · 1 min read · 9 views
#cs.LG #cs.AI #cs.CL

arXiv:2603.09983v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models enable scalable performance but face severe memory constraints on edge devices. Existing offloading strategies struggle with I/O bottlenecks due to the dynamic, low-information nature of autoregressive expert activation. In this paper, we propose to repurpose Speculative Decoding (SD) not merely as a compute accelerator, but as an informative lookahead sensor for memory management, supported by our theoretical and empirical analyses. Hence, we introduce MoE-SpAc, an MoE inference framework that integrates a Speculative Utility Estimator to track expert demand, a Heterogeneous Workload Balancer to dynamically partition computation via online integer optimization, and an Asynchronous Execution Engine to unify the prefetching and eviction in the same utility space. Extensive experiments on seven benchmarks demonstrate that MoE-SpAc achieves a 42% improvement in TPS over the SOTA SD-based baseline, and an average 4.04x speedup over all standard baselines. Code is available at https://github.com/lshAlgorithm/MoE-SpAc .

Executive Summary

This article proposes a novel approach to address memory constraints in Mixture-of-Experts (MoE) models on edge devices. MoE-SpAc integrates Speculative Decoding (SD) as an informative lookahead sensor for memory management, comprising a Speculative Utility Estimator, a Heterogeneous Workload Balancer, and an Asynchronous Execution Engine. The framework achieves significant improvements in throughput (42% over the SOTA SD-based baseline and 4.04x over all standard baselines) through efficient memory management and dynamic partitioning of computation. The code is publicly available on GitHub, facilitating replication and further research. MoE-SpAc's potential to scale MoE models on edge devices makes it a valuable contribution to the field of artificial intelligence and edge computing.

Key Points

  • ▸ MoE-SpAc integrates Speculative Decoding (SD) as an informative lookahead sensor for memory management
  • ▸ The framework consists of a Speculative Utility Estimator, a Heterogeneous Workload Balancer, and an Asynchronous Execution Engine
  • ▸ MoE-SpAc achieves significant improvements in throughput over existing baselines

Merits

Strength in Scalability

MoE-SpAc's ability to efficiently manage memory and dynamically partition computation enables scalable performance on edge devices, addressing a significant limitation of existing MoE models.

Innovative Approach

Repurposing Speculative Decoding as an informative lookahead sensor for memory management is a novel and effective solution to the memory constraints faced by MoE models on edge devices.

Empirical Validation

The framework's performance is extensively validated through experiments on seven benchmarks, providing strong evidence of its effectiveness.

Demerits

Limited Context

The article assumes a certain level of familiarity with MoE models and edge computing, which may limit its accessibility to researchers unfamiliar with these topics.

Technical Complexity

MoE-SpAc's architecture and implementation are technically complex, which may make it challenging for researchers to understand and reproduce the results.

Lack of Theoretical Foundations

While the article provides empirical evidence of MoE-SpAc's effectiveness, a more thorough analysis of its theoretical foundations and the underlying principles of Speculative Decoding would strengthen the framework's validity.

Expert Commentary

MoE-SpAc represents a significant advancement in the field of MoE models, addressing the critical challenge of memory constraints on edge devices. The framework's innovative approach to memory management and its ability to scale MoE models on edge devices make it a valuable contribution to the field of artificial intelligence and edge computing. However, the article's technical complexity and limited context may make it challenging for researchers to understand and reproduce the results. A more thorough analysis of the framework's theoretical foundations and the underlying principles of Speculative Decoding would strengthen its validity and facilitate wider adoption.

Recommendations

  • ✓ Further research is needed to fully understand the theoretical foundations of MoE-SpAc and the underlying principles of Speculative Decoding.
  • ✓ A more detailed analysis of the framework's performance in different edge computing scenarios would provide a more comprehensive understanding of its effectiveness.

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