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arxiv: 2604.19503 · v3 · submitted 2026-04-21 · 💻 cs.DC

Recognition: 2 theorem links

· Lean Theorem

ReaLB: Real-Time Load Balancing for Multimodal MoE Inference

Yingping Wang , Yi Wu , Xiangyu Wu , Junwei Cui , Weilin Cai , Zhijiang Guo , Jiayi Huang
Authors on Pith no claims yet

Pith reviewed 2026-05-12 01:19 UTC · model grok-4.3

classification 💻 cs.DC
keywords Mixture-of-ExpertsLoad BalancingMultimodal InferenceExpert ParallelismFP4 PrecisionReal-time OptimizationMoE InferenceVision Token Dominance
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The pith

ReaLB balances multimodal MoE inference by dynamically lowering precision on vision-heavy experts per rank with no added scheduling cost.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Mixture-of-Experts models for multimodal tasks face severe load imbalance when vision tokens dominate large-batch prefill stages under expert parallelism. ReaLB counters this by switching computation precision on the fly for overloaded ranks without introducing new overhead or extra memory. Vision-dominated ranks use lower-precision arithmetic on FP4 Tensor Cores while the change occurs inside the existing dispatch phase. Experiments on representative multimodal MoE models report end-to-end speedups between 1.10× and 1.32× with average accuracy loss kept under 1 percent. The method requires no redundant experts and performs layer-wise precision transformations at runtime.

Core claim

ReaLB dynamically adjusts the computation precision of MoE experts at runtime on a per-EP-rank basis. For ranks dominated by vision-heavy experts, it assigns lower-precision computation to exploit FP4 Tensor Cores and improve execution efficiency. The precision transformation occurs layer-wise on the fly and is hidden inside the dispatch phase before MoE computation begins, eliminating the need for redundant experts or additional memory allocation.

What carries the argument

Per-EP-rank runtime precision adjustment of experts, performed layer-wise and hidden inside the dispatch phase to enable FP4 Tensor Core usage on vision-heavy ranks.

If this is right

  • End-to-end inference throughput increases by 1.10× to 1.32× on representative multimodal MoE models.
  • Average accuracy degradation stays within 1 percent across tested workloads.
  • No additional memory or redundant expert copies are required.
  • Load imbalance from vision-token dominance during prefill is mitigated without new scheduling stages.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same per-rank precision switch could be tested on language-only or audio-heavy inputs to check whether the imbalance pattern generalizes.
  • Combining the dispatch-hidden transformation with existing quantization methods might compound throughput gains on the same hardware.
  • If the accuracy tolerance holds on larger batches, the approach could support higher concurrency without proportional hardware scaling.

Load-bearing premise

That reducing precision to FP4 for vision-dominated experts produces negligible impact on overall accuracy and that the transformation overhead stays fully hidden inside the existing dispatch phase.

What would settle it

Measure wall-clock time and task accuracy on a vision-heavy input batch using the full-precision baseline versus ReaLB-enabled runs on the same hardware and model.

Figures

Figures reproduced from arXiv: 2604.19503 by Jiayi Huang, Junwei Cui, Weilin Cai, Xiangyu Wu, Yingping Wang, Yi Wu, Zhijiang Guo.

Figure 1
Figure 1. Figure 1: Motivation for real-time load balancing in multimodal MoE inference. (EP), skewed expert selection causes significant inefficiency in both training and inference. This problem is more severe in multimodal MoE (MMoE) inference (Team, 2025c). Most prior work focuses on mitigating load imbalance dur￾ing MoE training. At the model level, auxiliary balancing losses (William et al., 2022; Dai et al., 2024) and d… view at source ↗
Figure 2
Figure 2. Figure 2: Model architecture of multimodal MoE. 2. Background & Related Work 2.1. Multimodal MoE Multimodal large language models (MLLMs) integrate text and vision processing within a unified architecture. Com￾pared to dense feed-forward layers, MoE layers provide higher capacity and better scalability for multimodal work￾loads. Most existing MMoE architectures combine a Vi￾sion Transformer (ViT), a vision language … view at source ↗
Figure 4
Figure 4. Figure 4: Dynamics of imbalance severity across iterations. rated into distinct instances. Under the continuous batching mechanism in vLLM, each batch may contain a mixture of prefill and decode requests. However, in large-batch iter￾ations, which represent the main regime where MoE load imbalance tends to become the dominant bottleneck, decode tokens typically account for a small fraction (usually no more than 10%)… view at source ↗
Figure 5
Figure 5. Figure 5: Dynamics of the top-1 hot device and top-1 hot expert across iterations, illustrating mismatch in history-based load pre￾dictions. Within a short window, the ratio of the most-loaded expert to the average expert ranges from 2× to 12×. At the device level, the peak load often exceeds the average by more than 2×, and in some cases, over 3×. This device-level imbal￾ance becomes more pronounced as the EP scale… view at source ↗
Figure 6
Figure 6. Figure 6: The system overview of ReaLB. ① Routing and Profiling: Input tokens are processed by the attention and gating modules on the local rank. This stage produces real-time expert routing decisions and token-level load statistics. ② Modality-aware LB Scheduling: The real-time modality-aware LB scheduler analyzes device-level imbalance based on the current routing results. It iden￾tifies overloaded ranks dominate… view at source ↗
Figure 9
Figure 9. Figure 9: ReaLB is enabled only in the compute-bound regime with large batch sizes. latency is dominated by non-GEMM operations, such as token alignment, activation functions, reduction, and ker￾nel launch overhead. In this regime, load imbalance across EP ranks has a negligible impact on overall latency; conse￾quently, enabling load balancing offers diminishing returns [PITH_FULL_IMAGE:figures/full_fig_p006_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Comparison of a single MMoE layer (i.e., layer-17) latency. 0 2 4 6 Rank Index 0 2 4 6 Mean Latency (ms) 1.17x 1.06x Baseline EPLB ReaLB 1 3 5 7 (a) Kimi-VL 0 2 4 6 Rank Index 0 1 2 3 4 5 Mean Latency (ms) 1.30x 0.95x Baseline EPLB ReaLB 1 3 5 7 (b) Qwen-VL 0 2 4 6 Rank Index 0 1 2 3 MoE Latency (ms) 1.26x 0.86x Baseline EPLB ReaLB 1 3 5 7 (c) ERNIE-VL [PITH_FULL_IMAGE:figures/full_fig_p007_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Comparison of MMoE layer latency across EP ranks at a representative iteration (i.e., layer-17, iter-110). capacity factor C = 1 and the modality threshold to 0.7. Models. We evaluate three recent open-source multimodal MoE models: Kimi-VL-A3B-Instruct (Du et al., 2025) (Kimi-VL), Qwen3-VL-30B-A3B-Instruct (Team, 2025c) (Qwen3-VL), and ERNIE-4.5-VL-27B-A3B (ERNIE Team, 2025) (ERNIE-VL). For modality-isola… view at source ↗
Figure 12
Figure 12. Figure 12: Comparison of End-to-End throughput with 5000 multimodal requests. show latency spikes: for EPLB, these likely result from extreme load imbalance due to its limited adjustment granu￾larity and redundant budget constraints; for ReaLB, spikes may arise when modality-threshold filtering protects text￾heavy hot devices, temporarily limiting acceleration. MoE Latency Distribution across EP Ranks [PITH_FULL_IM… view at source ↗
Figure 13
Figure 13. Figure 13: Dynamics of device-level load imbalance across MoE layers 12-21. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Overloaded devices and experts across iterations for layer 12-19, with token proportions for different modalities. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Routing characteristics across models and datasets. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Token composition in continuous batching. D. MoE layer Speedup under various batch sizes and image numbers [PITH_FULL_IMAGE:figures/full_fig_p017_16.png] view at source ↗
read the original abstract

Mixture-of-Experts (MoE) architectures are widely used in modern large language models and multimodal models. However, inference efficiency is often limited by highly dynamic and skewed expert workloads across different modalities. During the prefill stage with large batch sizes, vision tokens frequently dominate the input sequences. Under expert parallelism (EP), this leads to severe load imbalance, where a subset of devices becomes overloaded, reducing overall system throughput. We propose ReaLB, a real-time load balancing method for multimodal MoE (MMoE) inference that introduces zero scheduling overhead. ReaLB dynamically adjusts the computation precision of MoE experts at runtime on a per-EP-rank basis. For ranks dominated by vision-heavy experts, ReaLB assigns lower-precision computation to improve execution efficiency by exploiting FP4 Tensor Cores. ReaLB does not require redundant experts or additional memory allocation. Instead, it performs layer-wise expert precision transformation on the fly and hides the associated overhead within the dispatch phase before MoE computation. Experiments on representative MMoE models show that ReaLB achieves 1.10$\times$-1.32$\times$ end-to-end speedup while limiting average accuracy degradation to within 1%.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper proposes ReaLB, a real-time load balancing technique for multimodal MoE (MMoE) inference under expert parallelism. It dynamically reduces precision to FP4 for vision-dominated experts on overloaded ranks, performs layer-wise transformations on the fly, and hides the overhead inside the existing dispatch phase, claiming 1.10×–1.32× end-to-end speedup while keeping average accuracy degradation within 1% on representative MMoE models.

Significance. If the empirical results hold, ReaLB offers a memory-efficient way to mitigate modality-induced load imbalance in distributed MoE inference without redundant experts or extra scheduling. The approach is timely given hardware support for FP4 Tensor Cores and could improve throughput for large multimodal models during prefill stages with skewed vision-token workloads.

major comments (3)
  1. [Abstract] Abstract: the central speedup and accuracy claims (1.10×–1.32× with ≤1% degradation) are stated without naming the MMoE models tested, the baselines, number of runs, error bars, or per-phase timing breakdowns. These omissions make it impossible to verify whether the reported gains are robust or statistically significant.
  2. [Abstract] Abstract / method description: the claim that 'layer-wise expert precision transformation overhead can be completely hidden inside the dispatch phase' is load-bearing for the net speedup but is unsupported by any analysis of kernel launches, SM/memory-bandwidth contention with all-to-all communication, or whether FP4 conversion uses native Tensor Cores versus emulation.
  3. [Abstract] Abstract: no description is given of how vision-dominated experts are identified per EP rank or of any sensitivity study showing accuracy versus expert specialization and modality mix; without this, the bounded accuracy-loss claim cannot be assessed.
minor comments (1)
  1. [Abstract] The abstract refers to 'representative MMoE models' without naming them; this should be expanded for reproducibility even in the abstract.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important areas for improving clarity and verifiability in the abstract and supporting sections. We address each point below and have revised the manuscript accordingly to incorporate additional details and analysis.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central speedup and accuracy claims (1.10×–1.32× with ≤1% degradation) are stated without naming the MMoE models tested, the baselines, number of runs, error bars, or per-phase timing breakdowns. These omissions make it impossible to verify whether the reported gains are robust or statistically significant.

    Authors: We agree that the abstract would benefit from greater specificity to support verification of the claims. In the revised manuscript, we have updated the abstract to name the representative MMoE models evaluated (as detailed in Section 4 and Table 1), identify the baseline as standard expert parallelism without ReaLB, and note that speedups are averaged over multiple runs with error bars and per-phase breakdowns presented in Section 5.2 and Figure 6. These changes make the experimental context explicit while respecting abstract length constraints. revision: yes

  2. Referee: [Abstract] Abstract / method description: the claim that 'layer-wise expert precision transformation overhead can be completely hidden inside the dispatch phase' is load-bearing for the net speedup but is unsupported by any analysis of kernel launches, SM/memory-bandwidth contention with all-to-all communication, or whether FP4 conversion uses native Tensor Cores versus emulation.

    Authors: We acknowledge that the abstract does not include supporting analysis for the overhead-hiding claim. The mechanism is described in Section 3.3 of the full manuscript, but to strengthen the presentation we have added a dedicated overhead analysis in the revised Section 3.4. This includes profiling results on kernel launch times, SM and memory-bandwidth contention during overlapped all-to-all dispatch, and confirmation that FP4 conversion leverages native Tensor Core instructions on the evaluated hardware, resulting in negligible added latency. revision: yes

  3. Referee: [Abstract] Abstract: no description is given of how vision-dominated experts are identified per EP rank or of any sensitivity study showing accuracy versus expert specialization and modality mix; without this, the bounded accuracy-loss claim cannot be assessed.

    Authors: We agree that details on expert identification and sensitivity to modality mix are necessary to substantiate the accuracy claims. In the revised manuscript we have expanded the abstract to briefly describe the identification process (monitoring per-rank token modality histograms during dispatch with a 70% vision-token threshold) and added a sensitivity study in Section 4.3 plus Appendix B. The study varies modality ratios and expert specialization, confirming accuracy degradation remains within 1% across tested conditions. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical systems optimization without derivation chain

full rationale

The paper describes ReaLB as a runtime technique that dynamically lowers precision to FP4 for vision-heavy experts under EP and overlaps the transformation inside the existing dispatch phase. No equations, first-principles derivations, fitted parameters, or predictions appear in the provided text. The central claims (1.10–1.32× speedup with ≤1% accuracy loss) are supported solely by experimental measurements on representative MMoE models. No self-definitional steps, fitted-input-as-prediction, or load-bearing self-citations reduce any result to its own inputs by construction. The work is self-contained empirical systems research.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The method depends on hardware support for FP4 Tensor Cores and the empirical observation that vision tokens create load imbalance; no mathematical free parameters or invented entities are introduced.

axioms (2)
  • domain assumption FP4 Tensor Cores deliver faster execution with acceptable accuracy for vision-heavy experts in the tested models
    Invoked to justify the precision reduction step
  • ad hoc to paper Layer-wise expert precision transformation overhead can be completely hidden inside the dispatch phase
    Central to the zero-scheduling-overhead claim

pith-pipeline@v0.9.0 · 5524 in / 1311 out tokens · 45785 ms · 2026-05-12T01:19:24.731828+00:00 · methodology

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Works this paper leans on

2 extracted references · 2 canonical work pages

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    Dynamath: A dynamic visual benchmark for evaluating mathematical reasoning robustness of vision language models.arXiv preprint arXiv:2411.00836, 2024

    Curran Associates Inc. ISBN 9798331314385. Zou, C., Guo, X., Yang, R., Zhang, J., Hu, B., and Zhang, H. Dynamath: A dynamic visual benchmark for evalu- ating mathematical reasoning robustness of vision lan- guage models, 2025. URL https://arxiv.org/abs/ 2411.00836. 12 ReaLB: Real-Time Load Balancing for Multimodal MoE Inference A. Implementation Details M...

    work page arXiv 2025