ReaLB: A New Real-Time Load Balancing Scheme for Multimodal MoE Inference

ReaLB is a real-time load balancing scheme proposed to address the load imbalance issue in multimodal MoE inference. Its core is to achieve zero-overhead load balancing by dynamically adjusting the computational precision of experts, enabling a 1.29x speedup in multimodal MoE inference while keeping accuracy loss within 1.2%. This article will discuss it from aspects such as background, methods, experimental verification, and application scenarios.

Mixture of Experts (MoE) models face load imbalance challenges in inference deployment. In multimodal scenarios, visual tokens dominate, leading to overload on some devices while others remain idle. Traditional load balancing schemes have problems such as high scheduling overhead, resource redundancy, high memory overhead, and increased response latency. The dynamic distribution in multimodal scenarios further amplifies these issues.

The core innovation of ReaLB is to achieve load balancing by dynamically adjusting the computational precision of experts instead of traditional scheduling:

1. Zero scheduling overhead: Does not change expert allocation, only adjusts computational precision
2. Hierarchical precision adjustment: Takes EP-rank as the unit; ranks with heavy load use low precision (e.g., FP4), while light ones keep high precision, leveraging FP4 Tensor Core
3. Hidden conversion overhead: Precision conversion is parallel to the dispatch phase, transparent to users Technical advantages: No redundant experts needed, no additional memory allocation, real-time adaptation, hardware-friendly (utilizes low-precision capabilities of mainstream AI accelerators)

In experiments on representative MMoE models:

• Hierarchical speedup reaches 1.29x (inference time reduced by about 22%)
• Accuracy loss is controlled within 1.2%, with stable generalization across multiple downstream tasks This trade-off has high practical value for real-time applications (e.g., dialogue, interactive multimodal), where acceptable accuracy loss is exchanged for reduced latency.

ReaLB is particularly suitable for:

1. High-concurrency online services (large batches, mixed image-text input)
2. Heterogeneous cluster environments (inconsistent GPU models/memory)
3. Cost-sensitive deployments (need for accuracy-cost trade-off)

Limitations:

• Hardware dependency: FP4 Tensor Core is only supported by newer NVIDIA GPUs (e.g., Blackwell architecture)
• Precision granularity: Currently at rank level; finer granularity (expert/token level) is needed
• Theoretical analysis: Lack of theoretical research on accuracy loss bounds and optimal allocation strategies Future directions: Explore fine-grained precision control, supplement theoretical analysis, etc.

ReaLB is an important progress in MoE inference optimization, proving the potential of computational precision as a new dimension for load balancing, and providing new ideas for efficient inference system design. As multimodal large models are deployed, such system-level optimizations will become key supports.