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arxiv: 2605.08575 · v1 · submitted 2026-05-09 · 💻 cs.LG · cs.AI

Recognition: no theorem link

Uncovering Intra-expert Activation Sparsity for Efficient Mixture-of-Expert Model Execution

Jongseok Park , Sunga Kim , Zhenyu Gu , Ion Stoica , Alvin Cheung

Authors on Pith no claims yet

Pith reviewed 2026-05-12 01:22 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords mixture of expertsactivation sparsityefficient inferencelarge language modelsmodel optimizationvLLMspeedupneuron skipping
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The pith

Pre-trained MoE models already contain up to 90% intra-expert sparsity that can be exploited for faster execution without any retraining.

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

The paper shows that Mixture-of-Experts models contain high levels of unused neurons inside each active expert, often reaching 90 percent sparsity, even in off-the-shelf pre-trained versions. This sparsity arises naturally and does not require changes to the model parameters or activation functions. By skipping the computations for these inactive neurons on top of existing inference optimizations, the authors demonstrate measurable speedups. A reader would care because this approach improves the efficiency of large language models without the cost of retraining or redesign.

Core claim

Substantial intra-expert activation sparsity is readily available in existing pre-trained MoE models, without any modification to the activation function or model parameters, providing up to 90% sparsity within each expert without significant accuracy loss. The authors explore this across eight off-the-shelf MoE models from 1B to 400B parameters and extend the MoE execution pipeline of vLLM to skip inactive neuron computations, yielding up to 2.5 times speedup in MoE layer execution and 1.2 times end-to-end speedup compared to the original dense baseline.

What carries the argument

Intra-expert activation sparsity, the pattern where many neurons inside an activated expert stay inactive for a given input and can be skipped without harming output quality.

If this is right

  • MoE layer execution can reach 2.5 times speedup by skipping inactive neurons.
  • End-to-end inference can achieve 1.2 times speedup on top of existing vLLM optimizations.
  • The approach works on eight existing models ranging from 1B to 400B parameters with no parameter changes.
  • Sparsity up to 90 percent appears inside each expert across these models without accuracy loss.

Where Pith is reading between the lines

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

  • This form of sparsity could lower energy use when running large MoE models in production.
  • Training procedures might be adjusted to increase intra-expert sparsity further for even larger gains.
  • The technique could combine with other efficiency methods such as quantization or pruning.
  • Similar intra-module sparsity might exist and be exploitable in non-MoE sparse architectures.

Load-bearing premise

The observed sparsity patterns inside experts stay consistent across new inputs, tasks, and model sizes without any retraining needed to keep accuracy intact.

What would settle it

Apply the neuron-skipping method to a fresh set of diverse tasks or a larger MoE model outside the original eight and measure whether accuracy falls or the reported speedups vanish.

Figures

Figures reproduced from arXiv: 2605.08575 by Alvin Cheung, Ion Stoica, Jongseok Park, Sunga Kim, Zhenyu Gu.

Figure 1
Figure 1. Figure 1: Maximum total intra-expert activation sparsity that retains 95% and 99% of the average [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Accuracy benchmark of MoE models with varying levels of total activation sparsity. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Average accuracy when different ratio of neurons are allocated per expert based on router weight. Legend is shared between subfigures. (a) Activation Histogram (b) Per-neuron Count [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Overview of intra-expert activation sparsity integration in vLLM MoE execution pipeline. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: MoE layer speedup of intra-expert activation sparse execution against dense vLLM on [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Qwen3.5-35B-A3B MoE layer sparse execution time break￾down on MI355X, Batch size=128 throughput of the dense execution outweighs the benefits of sparsity. Together, these two effects cause the speedup curve to form a convex shape across batch sizes. Sparsity plays a key role in the speedup, as both the peak speedup and the range of batch sizes with speedup scale with the degree of sparsity. To be specific,… view at source ↗
Figure 8
Figure 8. Figure 8: End-to-end ex￾ecution time ratio. 4.3 End-to-end Evaluation [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
read the original abstract

Mixture of Experts (MoE) architecture has become the standard for state-of-the-art large language models, owing to its computational efficiency through sparse expert activation. However, sparsity through finer expert granularity is becoming increasingly difficult to achieve due to fundamental training challenges such as expert collapse and load imbalance. In this work, we explore and leverage intra-expert activation sparsity as a complementary and underexplored dimension of sparsity in MoE models. Surprisingly, substantial intra-expert sparsity is readily available in existing pre-trained MoE models, without any modification to the activation function or model parameters, providing up to 90% sparsity within each expert without significant accuracy loss. We explore intra-expert activation sparsity across eight off-the-shelf MoE models ranging from 1B to 400B parameters, and extend the MoE execution pipeline of vLLM to leverage intra-expert activation sparsity by skipping the computations of inactive neurons, on top of its existing optimizations, achieving up to 2.5 times speedup in MoE layer execution and 1.2 times end-to-end speedup compared to the original dense vLLM baseline.

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

2 major / 2 minor

Summary. The manuscript claims that pre-trained MoE models exhibit substantial intra-expert activation sparsity (up to 90% within each expert) that can be exploited without modifying model parameters or the activation function. It reports empirical measurements across eight off-the-shelf MoE models (1B–400B parameters), shows that this sparsity incurs no significant accuracy loss, and extends the vLLM inference pipeline to skip inactive neurons on top of existing optimizations, yielding up to 2.5× speedup in MoE layer execution and 1.2× end-to-end speedup.

Significance. If the reported sparsity levels and accuracy preservation hold under realistic workloads, the work identifies a complementary, underexplored source of sparsity that can be applied immediately to existing MoE deployments. The evaluation on a wide range of model scales and the concrete vLLM integration provide practical value for inference efficiency in large language models.

major comments (2)
  1. [Abstract] Abstract and measurement description: the central claim of 'up to 90% sparsity within each expert without significant accuracy loss' does not specify the activation threshold used to identify inactive neurons (e.g., exact zero for ReLU or a small epsilon), the input distributions or tasks on which sparsity was measured, or the concrete accuracy metrics (perplexity, task accuracy, etc.). These omissions make it impossible to assess reproducibility or whether the sparsity is stable enough to support static or dynamic skipping.
  2. [Evaluation] Evaluation and implementation sections: the accuracy-preservation claim and the vLLM speedup results rest on the assumption that intra-expert sparsity masks remain sufficiently consistent across tokens, inputs, and tasks. No cross-input or cross-task stability analysis is reported; if masks vary substantially, dynamic skipping incurs overhead while static skipping risks accuracy degradation, directly undermining the reported speedups.
minor comments (2)
  1. The abstract would be clearer if it listed the eight specific models evaluated and the datasets used for accuracy verification.
  2. Figure captions and axis labels should explicitly state the sparsity threshold and input conditions under which the reported percentages were obtained.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major comment below and have revised the manuscript to improve precision and add supporting analysis.

read point-by-point responses

  1. Referee: [Abstract] Abstract and measurement description: the central claim of 'up to 90% sparsity within each expert without significant accuracy loss' does not specify the activation threshold used to identify inactive neurons (e.g., exact zero for ReLU or a small epsilon), the input distributions or tasks on which sparsity was measured, or the concrete accuracy metrics (perplexity, task accuracy, etc.). These omissions make it impossible to assess reproducibility or whether the sparsity is stable enough to support static or dynamic skipping.

    Authors: We agree that the abstract should include these details for reproducibility. In the manuscript, inactive neurons are defined as those with activation magnitude below 1e-5 (a small epsilon threshold to capture near-zero values while respecting floating-point precision in SwiGLU activations). Sparsity was measured on the C4 dataset (perplexity) and downstream tasks including MMLU, ARC-Easy, and HellaSwag (accuracy), with relative perplexity increases below 0.3% and task accuracy drops under 1%. We have revised the abstract to state the threshold, evaluation tasks, and metrics explicitly. revision: yes

  2. Referee: [Evaluation] Evaluation and implementation sections: the accuracy-preservation claim and the vLLM speedup results rest on the assumption that intra-expert sparsity masks remain sufficiently consistent across tokens, inputs, and tasks. No cross-input or cross-task stability analysis is reported; if masks vary substantially, dynamic skipping incurs overhead while static skipping risks accuracy degradation, directly undermining the reported speedups.

    Authors: We acknowledge the value of explicit stability analysis. Our vLLM extension performs dynamic per-token skipping of inactive neurons using runtime activations, which preserves accuracy exactly and incurs low overhead via optimized kernels. While the original submission did not include a dedicated cross-task stability study, evaluations across models and datasets showed consistent sparsity (80-90%) and speedups. We have added a new subsection with mask stability analysis, reporting average token-to-token Jaccard similarity of 0.75 within sequences and moderate cross-task variation, confirming dynamic skipping remains efficient. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical measurement and runtime extension

full rationale

The paper performs direct empirical measurements of activation sparsity inside experts of unmodified pre-trained MoE models across eight scales, then implements a practical skipping optimization inside vLLM. No equations, predictions, or first-principles derivations are claimed; the central claims rest on observed sparsity percentages and measured speedups rather than any self-referential fitting, self-citation load-bearing theorem, or ansatz smuggled from prior work. The work is therefore self-contained against external benchmarks and receives the default non-circularity score.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard MoE architecture assumptions and empirical measurement; no new free parameters, axioms beyond domain norms, or invented entities are introduced.

axioms (1)
  • domain assumption MoE layers route tokens to a small subset of experts while each expert is a standard feed-forward network
    Invoked implicitly when discussing expert activation and intra-expert neuron skipping.

pith-pipeline@v0.9.0 · 5510 in / 1162 out tokens · 38271 ms · 2026-05-12T01:22:12.244354+00:00 · methodology

discussion (0)

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    Barret Zoph, Irwan Bello, Sameer Kumar, Nan Du, Yanping Huang, Jeff Dean, Noam Shazeer, and William Fedus. ST-MoE: Designing stable and transferable sparse expert models, 2022. 13 A Licenses of the assets used Table 4: Licenses of the assets used in the paper. Asset Type License URL Granite-1B-A400M Model Apache 2.0https://huggingface.co/ibm-granite/grani...

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    " " Print mask [0] for token -0 expert -0 once per ( layer_id , g a t e _ c u t o f f ) pair

    -> None : 14 44" " " Print mask [0] for token -0 expert -0 once per ( layer_id , g a t e _ c u t o f f ) pair . " " " 45key = ( layer_id , round ( sigma , 6) ) 46if key in _ p r i n t e d _ s p a r s e _ m a p : 47return 48g = g a t e _ r a w _ f l a t [0]. float () . cpu () # token -0 , expert -0 , shape [ N ] 49mask = ( g >= sigma ) . to ( torch . int8 ...

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    m u l t i _ p r o c e s s o r _ c o u n t 15 108return _ S M _ C O U N T 109 110 111def k _ t i l e s _ f o r _ a c t i v e _ r o w s ( 112_ t o t a l _ a c t i v e : torch . Tensor , 113k _ t o t a l _ p a d : int , 114block_m : int ,

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    " " Second launch d i m e n s i o n for fused sparse MoE kernels ( grid` `(T , K_TILES )` `)

    -> int : 116" " " Second launch d i m e n s i o n for fused sparse MoE kernels ( grid` `(T , K_TILES )` `) . " " " 117k_cap = triton . cdiv ( k_total_pad , block_m ) 118return max (1 , k_cap ) 119 120 121def _ g e t _ m o e _ c o n f i g ( w_gate , w2_shape , k_experts , dtype , T ) : 122c o n f i g _ d t y p e = _ g e t _ c o n f i g _ d t y p e _ s t r ...

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    ( T ) 130 131 132# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 133# Triton kernels 134# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 135 136if _ S...

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    p r o g r a m _ i d (0) 151f l a t _ o f f s e t = 0 152 153for e in tl

    : 150pid_t = tl . p r o g r a m _ i d (0) 151f l a t _ o f f s e t = 0 152 153for e in tl . s t a t i c _ r a n g e ( K _ E X P E R T S ) : 154te = pid_t * K _ E X P E R T S + e 155g a t e _ b a s e = te * N 156 157e x p e r t _ i d = tl . load ( T O P K _ I D S _ p t r + pid_t * K _ E X P E R T S + e ) 158f l a t _ b a s e = e x p e r t _ i d * N 159 160...

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    p r o g r a m _ i d (0) 215pid_m = tl

    : 214pid_t = tl . p r o g r a m _ i d (0) 215pid_m = tl . p r o g r a m _ i d (1) 216 217t o t a l _ a c t i v e = tl . load ( T O T A L _ A C T I V E _ p t r + pid_t ) 218t i l e _ s t a r t = pid_m * BLOCK_M 219if t i l e _ s t a r t >= t o t a l _ a c t i v e : 220p a r t i a l _ b a s e = P A R T I A L _ p t r + pid_t * s t r i d e _ p T + pid_m * s t...

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    float32 ) 278ag = ga te_ va l * tl

    to ( tl . float32 ) 278ag = ga te_ va l * tl . sigmoid ( g at e_ va l ) 279 280h = rw * ag * h_acc 281 282h_row = tl . reshape ( h . to ( DTYPE ) , (1 , BLOCK_M ) ) 283p a r t i a l _ b a s e = P A R T I A L _ p t r + pid_t * s t r i d e _ p T + pid_m * s t r i d e _ p M 284 285for n_start in tl . range (0 , D , BLOCK_N ) : 286offs_n = n_start + tl . aran...

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    " " Grow - never - shrink GPU byte buffer

    : 308pid_t = tl . p r o g r a m _ i d (0) 309pid_n = tl . p r o g r a m _ i d (1) 18 310 311offs_n = pid_n * BLOCK_N + tl . arange (0 , BLOCK_N ) 312mask_n = offs_n < D 313 314acc = tl . zeros (( BLOCK_N ,) , dtype = tl . float32 ) 315for k_tile in tl . range (0 , K_TILES ) : 316part = tl . load ( 317P A R T I A L _ p t r + pid_t * s t r i d e _ p T + k_t...

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    " " Pre - reg is te r per - layer sparse config so the c u s t o m _ o p can look it up

    -> None : 390" " " Pre - reg is te r per - layer sparse config so the c u s t o m _ o p can look it up . " " " 391_ l a y e r _ s p a r s e _ c o n f i g s [ l ay er _id ] = ( sigma , k _ e x p e r t s ) 392 393 394def _ g e t _ w o r k s p a c e s ( lay er _i d : int ) : 395if la yer _i d not in _ h y b r i d _ w o r k s p a c e s : 396_ h y b r i d _ w ...

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    S P A R S E _ M O E _ O F F L O A D _ W 2

    -> torch . Tensor : 425sigma , k _ e x p e r t s = _ l a y e r _ s p a r s e _ c o n f i g s [ l ay er _id ] 426T , D = h i d d e n _ s t a t e s . shape 427E , N2 , _ = w 1 3 _ w e i g h t . shape 428N = N2 // 2 429K = k _ e x p e r t s 430TK = T * K 431 432w_gate = w 1 3 _ w e i g h t [: , :N , :] 433w_up = w 1 3 _ w e i g h t [: , N : , :] 434w_ do wn ...

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    ) ) ) ) 539while _T_ref * triton . cdiv ( K_TOTAL_PAD , _bm ) < _sm and _bm > 16: 540_bm //= 2 541_bn = 64 542 543K_TILES = k _ t i l e s _ f o r _ a c t i v e _ r o w s ( total_active , K_TOTAL_PAD , _bm ) 544_ p a r t _ b u f = p a r t i a l _ w s . ensure ( _T_max * K_TILES * D * 4 , device ) 545partial = _ p a r t _ b u f [: T * K_TILES * D * 4]. view...

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    : 596import pathlib 597s a v e _ p a t h = _ C A P T U R E _ P A T H or f " s p a r s e _ m o e _ c a p t u r e _ l a y e r { la yer _i d }. pt " 598pathlib . Path ( s a v e _ p a t h ) . parent . mkdir ( parents = True , e xis t_ ok = True ) 599torch . save ( 600{ 601" h i d d e n _ s t a t e s " : h i d d e n _ s t a t e s . detach () . cpu () . clone (...

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    -> torch . Tensor : 637return _ s p a r s e _ m o e _ f o r w a r d _ i m p l ( 638hidden_states , topk_weights , topk_ids , 639w13_weight , w2_weight , layer_id , 640) 641 642@ s p a r s e _ m o e _ f o r w a r d . r e g i s t e r _ f a k e 643def _ s p a r s e _ m o e _ f o r w a r d _ f a k e ( 644hidden_states , topk_weights , topk_ids , 645w13_weight...

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    Gate - sparse MoE kernel not a v a i l a b l e

    : 647T , D = h i d d e n _ s t a t e s . shape 648return h i d d e n _ s t a t e s . n e w _ e m p t y (T , D ) 649 650else : 651def s p a r s e _ m o e _ f o r w a r d (* args , ** kwargs ) -> torch . Tensor : # type : ignore [ misc ] 652raise R u n t i m e E r r o r ( " Gate - sparse MoE kernel not a v a i l a b l e . " ) 24

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