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arxiv: 2606.04101 · v3 · pith:IZQRU7KHnew · submitted 2026-06-02 · 💻 cs.DC · cs.LG

UltraEP: Unleash MoE Training and Inference on Rack-Scale Nodes with Near-Optimal Load Balancing

Xinming Wei , Chao Jin , Tuo Dai , Yinmin Zhong , Shan Yu , Chengxu Yang , Bingyang Wu , Zili Zhang
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Jing Mai Qianchao Zhu Zhouyang Li Yuliang Liu Guojie Luo
This is my paper

Pith reviewed 2026-06-28 08:07 UTC · model grok-4.3

classification 💻 cs.DC cs.LG
keywords expert parallelismMoE load balancingrack-scale nodesreal-time rebalancingdistributed traininginference servingthroughput optimizationtoken all-to-all
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The pith

UltraEP rebalances MoE expert loads after every microbatch and layer on rack-scale nodes to reach 94.3 percent of ideal throughput.

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

The paper presents UltraEP as the first balancer that reacts to exact post-gating loads in real time rather than relying on historical averages. It does so by solving a quota-driven plan and moving expert states with low-overhead tile streaming that exploits the dense intra-rack links among dozens of GPUs. When applied to MoE models from 106 B to 671 B parameters across training and serving on up to 256 GPUs, the method keeps final rank imbalance between 1.01 and 1.04 and delivers a 1.49 times throughput gain over the unbalanced baseline while attaining 94.3 percent of the force-balanced ideal. The approach therefore removes the stragglers, all-to-all bottlenecks, and memory spikes that otherwise appear when expert loads vary rapidly in large expert-parallel deployments.

Core claim

UltraEP is the first exact-load, real-time balancer for large-EP MoE training and serving prefill on rack-scale nodes. It rebalances experts every microbatch and layer by combining an efficient quota-driven planner that reacts to post-gating load with RSN-native persistent tile streaming and relay-based fan-out mitigation for the resulting irregular expert-state transfers, attaining 94.3 percent of the force-balanced ideal throughput, a 1.49 times improvement over no balancing, and a reduction of inter-rank imbalance from the range 1.30-4.01 down to 1.01-1.04.

What carries the argument

The quota-driven planner that produces an exact rebalancing assignment from current post-gating loads together with the persistent tile streaming and relay fan-out that execute the resulting irregular expert-state transfers over rack-scale connectivity.

If this is right

  • MoE models up to 671 B parameters can run training and prefill serving on up to 256 GPUs while staying within 6 percent of theoretically perfect load balance.
  • Periodic historical balancers are no longer required once exact per-microbatch decisions become feasible.
  • Activation-memory spikes and token all-to-all contention are directly reduced by keeping expert counts nearly equal at every step.
  • The same rebalancing mechanism applies uniformly to both training and inference prefill phases.
  • Inter-rank imbalance can be driven from the 1.30-4.01 range down to 1.01-1.04 without changing model architecture or gating logic.

Where Pith is reading between the lines

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

  • The same per-microbatch planning could be applied inside a single node when tensor or pipeline parallelism already exists, provided the intra-node fabric is comparable.
  • Clusters that span multiple racks would need an additional slower inter-rack balancer layer to preserve the low-overhead property.
  • Hardware designers could use the demonstrated tolerance for irregular transfers as a target when sizing rack-scale fabrics for future MoE accelerators.
  • Production serving systems could drop conservative over-provisioning once load variation is handled at microbatch granularity rather than at epoch or hour granularity.

Load-bearing premise

Rack-scale nodes supply extended scale-up connectivity that permits low-overhead irregular expert-state transfers every microbatch and layer without exposing significant latency.

What would settle it

A throughput measurement on the same 256-GPU, 671 B model workload in which the rebalancing overhead exceeds the gains from reduced imbalance, causing overall performance to fall below the no-balancing baseline.

Figures

Figures reproduced from arXiv: 2606.04101 by Bingyang Wu, Chao Jin, Chengxu Yang, Guojie Luo, Jing Mai, Qianchao Zhu, Shan Yu, Tuo Dai, Xinming Wei, Yinmin Zhong, Yuliang Liu, Zhouyang Li, Zili Zhang.

Figure 1
Figure 1. Figure 1: UltraEP differs from prior solutions in load fidelity, decision timing, and balancing frequency. However, large-EP amplifies a fundamental challenge: ex￾pert load imbalance. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of expanded scale-up domain within a rack-scale node, compared with the standard RDMA cluster. to 256 GPUs, UltraEP sustains 94.6 % of the force-balanced ideal throughput in training and 93.9 % in serving prefill on average. It also improves training throughput by an av￾erage of 1.42× over Megatron-LM [57] and serving prefill throughput by 1.56× over SGLang [71], while keeping post￾balancing i… view at source ↗
Figure 3
Figure 3. Figure 3: An illustrative example of MoE forward under expert parallelism (EP): 4 experts, EP = 2, and top-𝑘 = 2. one RSN, keeping expert dispatch on the fast scale-up fabric rather than the slower scale-out network. 2.2 Distributed MoE Training and Inference MoE Architecture Evolution. Early MoE models (e.g., GShard [25], Mixtral [20], Switch Transformer [14]) adopt a coarse-grained design with a small number of la… view at source ↗
Figure 6
Figure 6. Figure 6: Rank-level imbalance before and after EPLB, com￾puted from previously recorded loads with EP=64. EPLB rebalancing interval is 50 batches for prefill and 3 global batches for training, respectively. Prefill (left) uses mixed data, while training (right) shows the 3510th global batch. that proactively equalizes experts does not eliminate the os￾cillation. Inter-microbatch jitter from sampling randomness also… view at source ↗
Figure 5
Figure 5. Figure 5: Training-time expert load distributions in the ini￾tial (first 25) and late (3500–3510 of 4500 total global batches) stages. Sampled on GLM4.5-106B-A12B [58] (top-8 activated of 128 experts, trained with GShard-style auxiliary loss) and DeepSeek-V3 [8] (top-8 activated of 256 experts, using DeepSeek-style auxiliary loss) within one EP64 group. and make the imbalance even less predictable. This yields a wor… view at source ↗
Figure 8
Figure 8. Figure 8: MoE forward pass with UltraEP enabled. Disp. MoE (Wgrad) MoE (Dgrad) Comb. Gate Attention Weight Distribution Replica Wgrad Reduction Disp. Comp. Stream Comm. Stream Layer i Layer i - 1 [PITH_FULL_IMAGE:figures/full_fig_p005_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: MoE backward pass with UltraEP enabled. weight/gradient buffer across layers. In Qwen3-235B-A22B (94 MoE layers, 128 experts), this reduces a single redun￾dant slot from 3.3 GB weights and 6.6 GB gradients to 36 MB and 72 MB per rank, at the cost of a tight, per-layer weight￾materialization deadline on the forward critical path (§4.2). 4.2 Computation-Communication Pipelines Forward: Eager Planning and Exp… view at source ↗
Figure 7
Figure 7. Figure 7: Expert layout and buffer management (example: 8 experts, single layer, EP = 4, 𝑁slot = 1). Redundant expert slots reuse weight and gradient buffers across layers, with no optimizer state. Main experts retain the full set of buffers. a logical expert or remains empty. This fixed layout keeps the runtime clean and deterministic, and yields a one-to￾many logical-to-physical mapping: each logical expert has on… view at source ↗
Figure 10
Figure 10. Figure 10: Relay schemes for hot expert fan-out, supposing one expert on rank 0 that multicasts to replica ranks 1–9, with ranks 2, 5, and 8 selected as relays. For clarity, the figure omits leaf ranks and finer-grained tiles. Each rank displays the state of send/receive channels along the timeline. more occupancy on the hot path to saturate RSN scale-up bandwidth, while preserving enough headroom for concur￾rent ov… view at source ↗
Figure 11
Figure 11. Figure 11: End-to-End Training Performance: Varying throughput across 20 training iterations on three models. 150 200 250 300 350 400 1.2 1.8 2.4 3 3.6 Avg TTFT (s) S E E+ O 1.0 2.0 3.0 4.0 Avg Imbalance 3.68 2.59 1.11 1.04 200 300 400 500 600 1.2 1.8 2.4 3 3.6 Avg TTFT (s) S E E+ O 1.0 2.0 3.0 Avg Imbalance 3.09 2.05 1.08 1.01 100 110 120 130 140 150 160 RPS 1.5 3 4.5 6 7.5 Avg TTFT (s) S E E+ O 1.0 2.0 3.0 4.0 Avg… view at source ↗
Figure 12
Figure 12. Figure 12: End-to-End Prefill Performance: RPS–mean TTFT trade-offs on two data domains and two models. • EPLB [12]: a widely used algorithm for computing bal￾anced expert placement plans, based on recent load. We optimize its integration into SGLang and Megatron-LM on RSNs for negligible balancing overhead. We use 50 pre￾fill steps and 3 global batches as the rebalancing frequency for serving and training, respecti… view at source ↗
Figure 14
Figure 14. Figure 14: Breakdown of peak GPU memory [PITH_FULL_IMAGE:figures/full_fig_p011_14.png] view at source ↗
Figure 17
Figure 17. Figure 17: Throughput and loss over RefMoE-288B training process with UltraEP enabled. Panel (b) plots the stable train￾ing phase after batch-size ramp-up. We sample no-balancing throughput from continuation-run intervals with UltraEP disabled. For the ideal, we report the best measured force￾balanced throughput to factor out environmental variability. UltraEP under identical balancing plans. We tune DeepEP for expe… view at source ↗
Figure 16
Figure 16. Figure 16: Communication latency of expert-weight distri￾bution under various imbalance levels (as in [PITH_FULL_IMAGE:figures/full_fig_p012_16.png] view at source ↗
read the original abstract

Large-scale expert parallelism (EP) is becoming pivotal for training and serving frontier MoE models, but it also amplifies device-level expert load imbalance into compute stragglers, token all-to-all bottlenecks, and activation-memory spikes. Existing balancers redistribute experts periodically based on historical load, which becomes unreliable for production deployments with non-stationary load patterns. We present UltraEP, the first exact-load, real-time balancer for large-EP MoE training and serving prefill on rack-scale nodes (RSNs). Leveraging the extended scale-up connectivity among dozens of GPUs within RSNs, UltraEP rebalances every microbatch and layer on critical paths, which requires nontrivial co-design of plan solving and expert replication communication to minimize exposed overhead. To this end, UltraEP eagerly reacts to post-gating load with an efficient quota-driven planner, and executes the resulting irregular expert-state transfers with RSN-native persistent tile streaming and relay-based fan-out mitigation. We evaluate UltraEP in a multi-RSN deployment of up to 256 GPUs, using cutting-edge MoE models from 106B to 671B parameters. Averaged across training and serving, UltraEP achieves 94.3% of the force-balanced ideal throughput, delivering 1.49$\times$ improvement over no-balancing, while reducing the final inter-rank imbalance from 1.30$-$4.01 to 1.01$-$1.04.

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 introduces UltraEP, the first exact-load real-time balancer for expert parallelism in large MoE models on rack-scale nodes. It uses a quota-driven planner reacting to post-gating loads and RSN-native persistent tile streaming with relay fan-out for irregular expert transfers every microbatch and layer. Evaluations on models from 106B to 671B parameters across up to 256 GPUs report that UltraEP reaches 94.3% of force-balanced ideal throughput (1.49× over no-balancing) while reducing inter-rank imbalance from 1.30-4.01 to 1.01-1.04.

Significance. If the throughput and imbalance claims hold with demonstrated low overhead, the work would be significant for production MoE training and serving by enabling dynamic, near-optimal load balancing at microbatch granularity on rack-scale hardware, addressing a key scalability bottleneck in expert parallelism.

major comments (3)
  1. [Abstract] Abstract: The central claims of 94.3% of ideal throughput and 1.49× improvement are presented as direct measurements but without error bars, workload/dataset details, or methodology, making independent verification of the performance numbers impossible from the provided information.
  2. [Evaluation section] Evaluation section: No quantitative breakdown or bound is given for the exposed latency of the quota-driven planner plus irregular expert-state transfers (persistent tile streaming and relay fan-out) under non-stationary loads; this overhead must be shown to be negligible relative to balance gains for the 94.3% ideal and final imbalance numbers (1.01-1.04) to hold.
  3. [Communication Design / Evaluation] The manuscript's performance claims rest on the assumption that rack-scale scale-up connectivity permits exact post-gating rebalancing every microbatch and layer with low exposed latency, yet no sensitivity analysis or timing measurements are reported to confirm this under the evaluated conditions.
minor comments (1)
  1. [Abstract] The abstract refers to 'nontrivial co-design' of plan solving and communication without summarizing the key co-design elements or their measured impact.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will incorporate revisions to improve clarity and completeness of the performance claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claims of 94.3% of ideal throughput and 1.49× improvement are presented as direct measurements but without error bars, workload/dataset details, or methodology, making independent verification of the performance numbers impossible from the provided information.

    Authors: We agree that the abstract lacks sufficient context for independent verification. In the revision we will expand it to briefly note the workloads (training and serving of 106B–671B MoE models), hardware (multi-RSN deployment up to 256 GPUs), and that the reported figures are averages across runs; full methodology, error bars, and dataset details already appear in Section 4 and will be cross-referenced. revision: yes

  2. Referee: [Evaluation section] Evaluation section: No quantitative breakdown or bound is given for the exposed latency of the quota-driven planner plus irregular expert-state transfers (persistent tile streaming and relay fan-out) under non-stationary loads; this overhead must be shown to be negligible relative to balance gains for the 94.3% ideal and final imbalance numbers (1.01-1.04) to hold.

    Authors: The current manuscript reports only end-to-end results. We will add a dedicated subsection with micro-benchmark timing of the quota-driven planner and the persistent-tile/relay communication primitives under the same non-stationary loads used in the main evaluation, showing that combined overhead remains below 6 % of per-layer compute time and is therefore negligible relative to the observed balance gains. revision: yes

  3. Referee: [Communication Design / Evaluation] The manuscript's performance claims rest on the assumption that rack-scale scale-up connectivity permits exact post-gating rebalancing every microbatch and layer with low exposed latency, yet no sensitivity analysis or timing measurements are reported to confirm this under the evaluated conditions.

    Authors: Section 3 describes the RSN-native mechanisms, but we acknowledge the absence of sensitivity data. We will add experiments that vary microbatch size and load non-stationarity while measuring exposed rebalancing latency, confirming that the scale-up fabric keeps latency low enough to support per-microbatch/layer rebalancing in the evaluated regimes. revision: yes

Circularity Check

0 steps flagged

No circularity; performance results are direct empirical measurements against explicit baselines.

full rationale

The paper describes an engineering system (UltraEP) for real-time expert rebalancing on rack-scale nodes and reports throughput and imbalance metrics from multi-GPU evaluations. These are presented as measured outcomes (94.3% of force-balanced ideal, 1.49× over no-balancing) without any claimed derivation, first-principles equations, fitted parameters renamed as predictions, or load-bearing self-citations. The evaluation section benchmarks against an external ideal and a no-balancing baseline; no step reduces the reported gains to inputs defined by the same experiment.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on hardware assumptions about rack-scale connectivity and workload non-stationarity; no free parameters or invented entities are introduced in the abstract.

axioms (2)
  • domain assumption Rack-scale nodes provide extended scale-up connectivity among dozens of GPUs enabling low-overhead irregular transfers every microbatch
    Invoked to justify real-time rebalancing feasibility without exposing overhead.
  • domain assumption Production MoE deployments exhibit non-stationary load patterns that render periodic historical balancers unreliable
    Used to motivate the need for exact-load real-time balancing.

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