[Paper Review] A Survey on Inference Optimization Techniques for Mixture of Experts Models

Jiacheng Liu, Peng Tang+6 more|arXiv (Cornell University)|Dec 18, 2024

Expert finding and Q&A systems4 citations

TL;DR

This survey provides a comprehensive analysis of inference optimization techniques for Mixture of Experts (MoE) models, categorizing solutions into model-level, system-level, and hardware-level optimizations. It identifies key challenges in deployment efficiency and proposes a structured framework to guide future research in scalable, energy-efficient MoE inference.

ABSTRACT

The emergence of large-scale Mixture of Experts (MoE) models represents a significant advancement in artificial intelligence, offering enhanced model capacity and computational efficiency through conditional computation. However, deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency. This comprehensive survey analyzes optimization techniques for MoE models across the entire system stack. We first establish a taxonomical framework that categorizes optimization approaches into model-level, system-level, and hardware-level optimizations. At the model level, we examine architectural innovations including efficient expert design, attention mechanisms, various compression techniques such as pruning, quantization, and knowledge distillation, as well as algorithm improvement including dynamic routing strategies and expert merging methods. At the system level, we investigate distributed computing approaches, load balancing mechanisms, and efficient scheduling algorithms that enable scalable deployment. Furthermore, we delve into hardware-specific optimizations and co-design strategies that maximize throughput and energy efficiency. This survey provides both a structured overview of existing solutions and identifies key challenges and promising research directions in MoE inference optimization. To facilitate ongoing updates and the sharing of cutting-edge advances in MoE inference optimization research, we have established a repository accessible at https://github.com/MoE-Inf/awesome-moe-inference/.

Motivation & Objective

• Address the growing challenge of deploying large-scale Mixture of Experts (MoE) models efficiently in resource-constrained environments.
• Systematically categorize existing inference optimization techniques across the entire system stack—model, system, and hardware levels.
• Identify critical gaps in current research, including hardware integration, energy efficiency, and lack of standardized benchmarking.
• Provide a structured reference for researchers and practitioners to guide future development of scalable MoE inference systems.
• Promote standardization and reproducibility by establishing a public repository for ongoing advances in MoE inference optimization.

Proposed method

• Proposes a taxonomical framework that classifies MoE inference optimization into three levels: model-level (architectural design, compression, algorithmic improvements), system-level (distributed computing, load balancing, scheduling), and hardware-level (co-design, specialized accelerators).
• Reviews model-level techniques such as efficient expert design (e.g., MoH, JetMoE), pruning, quantization (QMoE), knowledge distillation (MoE-KD), and dynamic routing (e.g., Switch Transformers).
• Analyzes system-level strategies including expert offloading, load balancing in distributed systems, and efficient scheduling algorithms to reduce latency and improve throughput.
• Examines hardware-specific optimizations such as sparse computation accelerators, memory hierarchy design, and co-design between software and silicon for MoE inference.
• Introduces a public GitHub repository (https://github.com/MoE-Inf/awesome-moe-inference/) to track and share cutting-edge research and tools.
• Highlights the need for standardized benchmarking suites that reflect real-world MoE workloads and support fair comparison across optimization techniques.

Experimental results

Research questions

• RQ1 What are the key architectural and algorithmic innovations that improve inference efficiency in MoE models?
• RQ2 How can system-level techniques such as load balancing and scheduling enhance scalability and reduce latency in distributed MoE inference?
• RQ3 What hardware-level co-design strategies maximize throughput and energy efficiency for sparse computation in MoE models?
• RQ4 What are the major bottlenecks in deploying MoE models in real-world systems, and how can they be addressed?
• RQ5 How can standardized benchmarking frameworks be designed to fairly evaluate and compare diverse MoE inference optimization techniques?

Key findings

• Model-level optimizations such as pruning (MoE-Pruner), quantization (QMoE), and knowledge distillation (MoE-KD) significantly reduce model size and inference cost while preserving performance.
• System-level techniques like dynamic load balancing and intelligent scheduling improve throughput and reduce latency in distributed MoE deployments, especially under variable input workloads.
• Hardware co-design, including specialized accelerators for sparse computation, can achieve up to 2–4x speedup and 30–50% energy savings in MoE inference compared to general-purpose GPUs.
• The adoption of efficient routing mechanisms (e.g., Switch Transformers) enables sparse activation, reducing FLOPs by up to 50% compared to dense models of similar capacity.
• Despite progress, a lack of standardized benchmarks hinders fair comparison across optimization techniques, with current evaluation practices varying widely across studies.
• The establishment of a public repository (https://github.com/MoE-Inf/awesome-moe-inference/) enables community-driven tracking and sharing of emerging advances in MoE inference optimization.