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arxiv: 2510.08055 · v2 · submitted 2025-10-09 · 💻 cs.LG · cs.DC

From Tokens to Layers: Redefining Stall-Free Scheduling for MoE Serving with Layered Prefill

Gunjun Lee , Jiwon Kim , Jaiyoung Park , Younjoo Lee , Jung Ho Ahn This is my paper

Pith reviewed 2026-05-18 08:44 UTC · model grok-4.3

classification 💻 cs.LG cs.DC
keywords Mixture-of-ExpertsLLM inference servingprefill schedulingstall-free decodinglayer partitioningenergy efficiencyMoE weight loading
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The pith

Layered prefill partitions MoE models into contiguous layer groups to interleave prefill and decode, sustaining stall-free operation while cutting TTFT by up to 70% and energy by 22%.

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

The paper introduces layered prefill for Mixture-of-Experts LLM serving to address limitations of chunked prefill. Chunked prefill splits long prompts by tokens and interleaves with decode but causes redundant expert weight loads in MoE models, increasing memory traffic by up to 39%. Layered prefill instead partitions the model vertically into contiguous layer groups and interleaves prefill and decode across these groups. This maintains stable time-between-token while reducing off-chip bandwidth demand. Evaluations indicate it improves the TTFT-TBT trade-off and lowers per-token energy consumption.

Core claim

By shifting the scheduling axis from tokens to layers, layered prefill treats contiguous layer groups as atomic scheduling units. It interleaves prefill and decode across these groups to achieve stall-free decoding without the chunk-induced MoE weight reloads, thereby lowering TTFT by up to 70%, end-to-end latency by 41%, and per-token energy by up to 22%.

What carries the argument

The layered prefill scheduler, which vertically partitions the transformer into contiguous layer groups and interleaves prefill and decode operations across these groups instead of token chunks.

If this is right

  • Reduces off-chip bandwidth demand by eliminating redundant expert weight loads.
  • Lowers TTFT by up to 70% while preserving stall-free decoding.
  • Decreases end-to-end latency by 41% and per-token energy by up to 22%.
  • Consistently improves the TTFT-TBT Pareto frontier over chunked prefill.
  • Lowers expert-load traffic and energy cost in co-located prefill-decode environments.

Where Pith is reading between the lines

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

  • Similar layer-group scheduling might benefit dense transformer models by reducing activation movement even without experts.
  • The approach could extend to distributed serving setups where layer groups align with device boundaries.
  • Future work might explore dynamic group sizing based on prompt length or model depth to optimize further.
  • Combining layered prefill with other memory optimizations could yield additional gains in energy efficiency.

Load-bearing premise

Contiguous layer groups can be scheduled as atomic units without violating sequential data dependencies between layers or introducing synchronization overhead that reintroduces stalls.

What would settle it

An experiment measuring whether synchronization costs between layer groups exceed the bandwidth savings from avoided expert reloads, or whether TBT exceeds targets for models with high interconnect latency.

Figures

Figures reproduced from arXiv: 2510.08055 by Gunjun Lee, Jaiyoung Park, Jiwon Kim, Jung Ho Ahn, Younjoo Lee.

Figure 1
Figure 1. Figure 1: (Upper right) Per iteration, chunked prefill splits an input prompt into multiple chunks, and at each iteration one chunk is processed in order from the beginning with the decode. (Lower right) For layered prefill, exactly one layer group performs both prefill and decode, while the others perform decode only. Prefill advances by one group per iteration, maintaining stall-free decoding. SLOs, service provid… view at source ↗
Figure 2
Figure 2. Figure 2: (Left) MoE weight loading vs. chunk size. The hatched region indicates the MoE weights loaded by a single chunk. (Right) runtime of each kernel vs. chunk size. We fix the input length fixed at 8,192 tokens [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: SLO attainment under different request rates. The red horizontal line marks the effective SLO attainment threshold (90%). layered prefill across request rates for two models (Qwen and GPT) and two workloads (arXiv and ShareGPT). Qwen: (a) On arXiv, layered prefill sustains ≈100% SLO attainment through 1.7 req/s, while chunked prefill collapses by 1.5; at 1.8 req/s layered prefill remains well above chun￾ke… view at source ↗
Figure 5
Figure 5. Figure 5: Token generation over time on arXiv with Qwen. pare cumulative token output for a single request on Qwen using the arXiv workload at a request rate of 1.3 req/s un￾der chunked prefill and layered prefill. The steeply rising middle interval reflects the period when layered prefill has quickly finished other requests’ prefills and runs in decode￾only mode, so token generation accelerates. These factors reduc… view at source ↗
read the original abstract

Large Language Model (LLM) inference in production must meet stringent service-level objectives for both time-to-first-token (TTFT) and time-between-token (TBT) while maximizing throughput under fixed compute, memory, and interconnect budgets. Modern serving systems adopt stall-free scheduling techniques such as chunked prefill, which splits the processing of long prompts along the token dimension and interleaves prefill with ongoing decode iterations. While effective at stabilizing TBT, chunked prefill incurs substantial overhead in Mixture-of-Experts (MoE) models: redundant expert weight loads increase memory traffic by up to 39% and inflate energy consumption. We propose layered prefill, a new scheduling paradigm that treats transformer layer groups as the primary scheduling unit, specifically targeting MoE serving. By vertically partitioning the model into contiguous layer groups and interleaving prefill and decode across the groups, layered prefill sustains stall-free decoding while eliminating chunk-induced MoE weight reloads. It reduces off-chip bandwidth demand, lowering TTFT by up to 70%, end-to-end latency by 41% and per-token energy by up to 22%. Evaluations show that layered prefill consistently improves the TTFT--TBT Pareto frontier over chunked prefill, reducing expert-load traffic and energy cost while maintaining stall-free decoding. Overall, shifting the scheduling axis from tokens to layers unlocks a new operating regime for high-efficiency, energy-aware MoE serving in co-located environments.

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

1 major / 2 minor

Summary. The paper proposes layered prefill, a scheduling paradigm for MoE LLM serving that vertically partitions the model into contiguous layer groups and interleaves prefill and decode across groups. This replaces token-dimension chunking to eliminate redundant expert weight reloads while preserving stall-free TBT, with reported gains of up to 70% lower TTFT, 41% lower end-to-end latency, and 22% lower per-token energy.

Significance. If validated, the shift from token-based to layer-based scheduling granularity could meaningfully improve memory bandwidth efficiency and energy use in co-located MoE inference without sacrificing latency SLOs. The empirical Pareto-frontier improvements over chunked prefill represent a concrete contribution to serving-system design for large expert models.

major comments (1)
  1. The central claim depends on treating contiguous layer groups as atomic scheduling units for interleaving prefill and decode. Sequential hidden-state dependencies between layers imply that group boundaries may require buffering, barriers, or pipeline flushes; the manuscript provides no analysis of the resulting synchronization or memory-traffic overhead as a function of group size, model depth, or interconnect latency. This premise is load-bearing for the reported 70% TTFT and 41% latency reductions.
minor comments (2)
  1. Experimental results report concrete percentage gains but omit error bars, the exact layer-group sizes used, and a breakdown of how much improvement derives from reduced expert reloads versus other factors.
  2. Notation for group boundaries and the precise interleaving schedule should be formalized (e.g., with a diagram or pseudocode) to clarify dataflow across groups.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the potential significance of shifting scheduling granularity from tokens to layers in MoE serving. We address the major comment point by point below. We will incorporate additional analysis in the revised manuscript to strengthen the treatment of synchronization and overhead at layer-group boundaries.

read point-by-point responses

  1. Referee: The central claim depends on treating contiguous layer groups as atomic scheduling units for interleaving prefill and decode. Sequential hidden-state dependencies between layers imply that group boundaries may require buffering, barriers, or pipeline flushes; the manuscript provides no analysis of the resulting synchronization or memory-traffic overhead as a function of group size, model depth, or interconnect latency. This premise is load-bearing for the reported 70% TTFT and 41% latency reductions.

    Authors: We thank the referee for this important observation. Within each contiguous layer group, layers execute sequentially with direct hidden-state handoff exactly as in a standard forward pass; no extra buffering is introduced inside the group. Interleaving between prefill and decode occurs only at group boundaries, which serve as natural synchronization points where the scheduler can context-switch requests. This design mirrors the boundary handling already present in pipeline-parallel inference but applied to prefill-decode co-location. We acknowledge that the submitted manuscript did not contain a dedicated quantitative analysis of synchronization or memory-traffic overhead as a function of group size, depth, or interconnect latency. In the revision we will add a new subsection that (1) provides an analytical model of the additional traffic at group boundaries, (2) reports micro-benchmark measurements for group sizes of 2, 4, and 8 layers on the evaluated models, and (3) shows that the incremental overhead remains below 5 % of total memory traffic and is more than offset by the elimination of redundant expert weight loads. These additions will directly support the reported TTFT and latency gains. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical scheduling improvements rest on measurements, not self-referential derivation

full rationale

The paper introduces layered prefill as a scheduling paradigm that partitions the model into contiguous layer groups and interleaves prefill/decode to avoid MoE weight reloads while preserving stall-free operation. All reported gains (up to 70% TTFT reduction, 41% end-to-end latency, 22% energy) are presented as results of evaluations and direct measurements on the proposed system. No equations, fitted parameters, or first-principles derivations appear in the abstract or description that would allow a quantity to be redefined in terms of itself. The design choice of treating layer groups as atomic units is an engineering assumption whose validity is checked empirically rather than derived by construction from prior results or self-citations. The derivation chain is therefore self-contained and independent of the target performance numbers.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard assumptions about transformer layer dependencies and MoE expert activation patterns; no new physical constants or ad-hoc fitted scalars are introduced in the abstract.

axioms (2)
  • domain assumption Transformer layers must execute sequentially within a forward pass; data dependencies between consecutive layers cannot be violated by the scheduler.
    Implicit in any layer-group interleaving scheme; if false, the proposed vertical partitioning would produce incorrect outputs.
  • domain assumption Expert weights for a given layer group remain resident in on-chip memory for the duration of that group's prefill or decode work.
    Required for the elimination of redundant off-chip loads; stated as the mechanism that removes chunk-induced reloads.

pith-pipeline@v0.9.0 · 5811 in / 1645 out tokens · 26746 ms · 2026-05-18T08:44:10.942242+00:00 · methodology

discussion (0)

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