MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism

Serving MoE turns sparse compute into a scheduling problem: routing skew can create stragglers and token-dispatch communication can bottleneck, motivating scheduling and, in systems such as MegaScale-Infer, disaggregated expert-parallel serving.

published · difficulty 4/5 · 20 min read

Reading map and next steps

Intuition

Build the mental picture first so the rest of the page has something to attach to.

PredictName the object in plain language, then predict what should change.Leave with one reusable mental picture before notation appears.

MoE looks like "cheaper inference": only run a few experts per token.

In serving, the story flips. MoE becomes a systems scheduling problem:

tokens get routed unevenly, so some experts become bottlenecks (stragglers),
expert-parallel MoE layers must dispatch token activations to the selected experts and combine results; this is often all-to-all in colocated expert parallelism and becomes M2N/N2M in disaggregated layouts such as MegaScale-Infer,
at decode time, you also carry the KV cache burden, so you are juggling both memory and communication.

MegaScale-Infer illustrates the systems goal: keep GPUs busy despite sparsity, skew, and communication overhead. The biggest wins are not guaranteed by sparse FLOPs alone; they come from the surrounding scheduling, batching, and communication plan.

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Math

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Equation 1Inspect equation objectBytes_{comm} \approx 2\cdot T\cdot k\cdot d_{model}\cdot b,Equation 2Inspect equation objectt_{layer} \approx \max_e t_e \propto \max_e n_e.

Token dispatch communication (why layout matters)

Per MoE layer, tokens are routed to experts and then combined back. A crude but useful byte-count is:

\mathrm{Bytes}_{\mathrm{comm}} \approx 2\cdot T\cdot k\cdot d_{\mathrm{model}}\cdot b,

where:

$T$ is tokens in the microbatch,
$k$ is top- $k$ experts per token,
$d_{\mathrm{model}}$ is hidden width,
$b$ is bytes per element (e.g., 2 for fp16),
the factor of 2 is dispatch + combine.

If communication is the bottleneck, adding more experts can make you slower even if compute drops.

Stragglers from routing skew

Let $n_e$ be the number of tokens routed to expert $e$ in a microbatch. If expert compute time is proportional to token count, then layer time is dominated by:

t_{\mathrm{layer}} \approx \max_e\; t_e \propto \max_e\; n_e.

Even if the average load is small, the maximum load can be much larger when routing is skewed. Tail latency kills throughput.

Disaggregation (separate pools)

One modern response is disaggregation: run attention in one pool of GPUs and experts in another, pipelining between them. This trades extra communication for higher utilization and better resource matching.

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Code

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Code witness 1import numpy as np rng = np.random.default_rng(0) E = 16 # experts T = 4096 # tokens in a mic...python

import numpy as np

rng = np.random.default_rng(0)
E = 16        # experts
T = 4096      # tokens in a microbatch

# A skewed routing distribution (one "popular" expert)
p = np.ones(E) * 0.9
p[0] = 6.0
p = p / p.sum()

choices = rng.choice(E, size=T, p=p)  # top-1 for simplicity
counts = np.bincount(choices, minlength=E)

print("avg tokens/expert:", round(float(counts.mean()), 2))
print("max tokens/expert:", int(counts.max()))
print("straggler factor max/mean:", round(float(counts.max() / counts.mean()), 2))
print("top-5 expert loads:", np.sort(counts)[-5:][::-1].tolist())

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Interactive Demo

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Live Concept Demo

Explore MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism

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difficulty 4/5graduatecode-aligned

Demo inquiry checkpoint

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01Choose lensTrace a quantity

02ObserveDemo state pending

03GroundName the equation, invariant, or control that explains it.

04CarryNext: Speculative Decoding: Lossless Multi-Token Generation

Choose what to inspect in MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism. This shared fallback is an observation guide, not evidence of learning.

Loading interactive demo...

The demo below asks you to predict the serving bottleneck before revealing expert loads and token-dispatch communication bytes. The key invariant is that sparse activated compute does not guarantee low latency: routing skew and dispatch/combine traffic can bottleneck the layer.

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Object

4/4 sections ready

Concept: MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism

What is the smallest example that makes MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism click without losing the math?

BeforeScaled Dot-Product Attention & Transformer LayersNow4/4 sections readyTryManipulate one control and predict the visible change.NextSpeculative Decoding: Lossless Multi-Token Generation

Object contextLLM Systems

StateLLM Systems

3 sources attachedChecking local snapshot

concept:llm-systems/moe-serving

Math2 equation objectsBytes_{comm} \approx 2\cdot T\cdot k\cdot d_{model}\cdot b,CodeMoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated P...rng = np.random.default_rng(0)DemoMoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated P...Manipulate one control and predict the visible change.

ConceptLearner lens

MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism

What is the smallest example that makes MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism click without losing the math?

Mode questionCan I say the mechanism back in one sentence before I reveal anything?

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01PredictCommit before reveal.02ExplainName the invariant.03Ground3 source links nearby.04ExperimentChange one knob.05CarrySave the next question.

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4/4 sections ready

Carry inScaled Dot-Product Attention & Transformer Layers

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Work hereMoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism

Carry outSpeculative Decoding: Lossless Multi-Token Generation

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ConceptMoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated ParallelismLLM Systems

01MechanismInspect motionSee how the idea changes form before adding more explanation.02VisualChoose a cueBind one visible mark to one mathematical role.03SourcesCheck boundary3 source links attached to the object.04ClaimAudit evidenceTreat the page claim as something to verify.05PracticeAttempt transferWrite before feedback so the model has something to repair.06RoomAsk groundedAttach the question to the same exact object.

Mechanism Storyboard

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Demo notes open01 / Intuition

Editorial systems illustration of sparse token routing across experts with all-to-all exchange and a straggler bottleneck.

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Commit first

Before reading further, choose the kind of change MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism should make visible.

Visual Inquiry

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4/4 stages readyDemo notes connected

Visual cueWhich visible object should carry the first intuition?

Inspection depth2/4

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Object - ConceptMoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated ParallelismQuestion

What is the smallest example that makes MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism click without losing the math?

concept:llm-systems/moe-serving

Boundary

sources: shazeer-2017-sparsely-gated-moe, fedus-2021-switch-transformer, zhu-2025-megascale-infer

Check

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Evidence

3 selected-object sources shown first; 3 references total.

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selected object source · paper · 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts LayerShazeer et al.

Located CF editorial boundary

Grounds sparse expert routing and why gating turns capacity into dispatch and load-balance constraints.

Used here as

Shazeer supports sparse top-k MoE routing plus importance/load-balancing losses for uneven expert use. Fedus supports top-1 routing, expert-capacity overflow, auxiliary load balancing, an...

Caveat

Checks sparse routing, uneven expert load, and load-balancing/scheduling only. Shazeer/Fedus ground model/distributed mechanics; MegaScale-Infer supplies serving scope. Local byte-count,...

Open source

selected object source · paper · 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient SparsityFedus, Zoph, and Shazeer

Located CF editorial boundary

Grounds top-1 routing, capacity factors, and communication/training-stability caveats relevant to serving.

Used here as

Shazeer supports sparse top-k MoE routing plus importance/load-balancing losses for uneven expert use. Fedus supports top-1 routing, expert-capacity overflow, auxiliary load balancing, an...

Caveat

Checks sparse routing, uneven expert load, and load-balancing/scheduling only. Shazeer/Fedus ground model/distributed mechanics; MegaScale-Infer supplies serving scope. Local byte-count,...

Open source

selected object source · paper · 2025MegaScale-Infer: Serving Mixture-of-Experts at Scale with Disaggregated Expert ParallelismZhu et al.

Located CF editorial boundary

Serving-specific evidence for MoE decoding sparsity, attention/FFN disaggregation, M2N token-routing communication, and production expert-load imbalance.

Used here as

Shazeer supports sparse top-k MoE routing plus importance/load-balancing losses for uneven expert use. Fedus supports top-1 routing, expert-capacity overflow, auxiliary load balancing, an...

Caveat

Checks sparse routing, uneven expert load, and load-balancing/scheduling only. Shazeer/Fedus ground model/distributed mechanics; MegaScale-Infer supplies serving scope. Local byte-count,...

Open source

Claim Review

Serving MoE turns sparse compute into a scheduling problem: routing skew can create stragglers and token-dispatch communication can bottleneck, motivating scheduling and, in systems such as MegaScale-Infer, disaggregated expert-parallel serving.

Object - ConceptMoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated ParallelismQuestion

What is the smallest example that makes MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism click without losing the math?

concept:llm-systems/moe-serving

Boundary

sources: shazeer-2017-sparsely-gated-moe, fedus-2021-switch-transformer, zhu-2025-megascale-infer

Check

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1 CF editorial source-scope review recorded

Publisher-side editorial review is not independent replication. Claims without it still need exact source-support review. 3 references and 3 local witnesses are available for inspection.

MoE inference and serving use sparse routing: a gate sends each token to a small subset of experts, so per-expert token counts can become uneven and require load-balancing, capacity buffers, or scheduling/parallelism choices.

Registered CF editorial boundary

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts LayerGrounds sparse expert routing and why gating turns capacity into dispatch and load-balance constraints.Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient SparsityGrounds top-1 routing, capacity factors, and communication/training-stability caveats relevant to serving.MegaScale-Infer: Serving Mixture-of-Experts at Scale with Disaggregated Expert ParallelismServing-specific evidence for MoE decoding sparsity, attention/FFN disaggregation, M2N token-routing communication, and production expert-load imba...

Used here as

Shazeer supports sparse top-k MoE routing plus importance/load-balancing losses for uneven expert use. Fedus supports top-1 routing, expert-capacity overflow, auxiliary load balancing, and communication-cost...

Local witness

source-span-shazeer-2017-sparsely-gated-moe source-span-fedus-2021-switch-transformer source-span-zhu-2025-megascale-infer math-object-2 code-witness-1 interactive-demo

Equation 2

t_{\mathrm{layer}} \approx \max_e\; t_e \propto \max_e\; n_e.

Code witness 1import numpy as np rng = np.random.default_rng(0) E = 16 # experts T = 4096 # tokens in a mic...

Caveat

Checks sparse routing, uneven expert load, and load-balancing/scheduling only. Shazeer/Fedus ground model/distributed mechanics; MegaScale-Infer supplies serving scope. Local byte-count, straggler formula, c...

Review stateCF editorial source-scope reviewClaim metadata: source checkedPublisher-side editorial review only; not independent replication. Check caveats and exact source scope.

Shazeer 2017 supports sparse top-k MoE routing and load/importance-balancing losses for uneven expert use. Fedus 2021 supports top-1 routing, capacity overflow, auxiliary load balancing, and communication-cost tradeoffs. MegaScale-Infer 2025 supplies serving evidence for MoE decoding, token dispatch, FFN underutilization from sparsity, attention/FFN disaggregation, M2N/N2M communication in that setup, and production expert-load imbalance. Local math/code/demo remain toy witnesses.

Reviewer: codex+oracle; reviewed 2026-05-07

Practice · MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism

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MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism

Source boundary: sources: shazeer-2017-sparsely-gated-moe, fedus-2021-switch-transformer, zhu-2025-megascale-infer

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LLM Systems

concept:llm-systems/moe-serving

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Selected object routeAsk from this object; carry one invariant back.sources: shazeer-2017-sparsely-gated-moe, fedus-2021-switch-transformer, zhu-2025-megascale-infer

ObjectConceptMoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated P...
PredictBefore revealMoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated P...
WitnessCompare codeMoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated P...
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ConceptMoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated ParallelismLLM Systems

Code witness comparisonMoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism code wi...rng = np.random.default_rng(0)Prediction before revealMoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism predictionManipulate one control and predict the visible change.

Grounded room questionWhat is the smallest example that makes MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism click without losing the math?Checking local snapshot

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conceptLLM Systems

MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism

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What is the smallest example that makes MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism click without losing the math?

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I am working in Continuous Function's research reading room. Object: concept - MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism Object key: concept:llm-systems/moe-serving Context: LLM Systems Anchor id: concept/concept-notebook/llm-systems/moe-serving Open question: What is the smallest example that makes MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism click without losing the math? Evidence to inspect: - Source ids to inspect: shazeer-2017-sparsely-gated-moe, fedus-2021-switch-transformer, zhu-2025-megascale-infer - Definition, prerequisite, and contrast concept links - The equation or code witness that makes the concept operational - One demo state that shows the invariant instead of a slogan Deterministic role lenses for this object: - Boundary: fixed perspectives, not people, community contributions, or independent review - Source-checking summary: Treat this as a mechanism object: connect the definition to one equation, code witness, or demo before broadening the discussion. - Proposed experiment: Ask the learner to perturb one representation, then check whether the same invariant survives in math, code, and demo. - Teach/transfer move: Turn the mechanism into one sentence that predicts a neighboring concept. - Assumptions: - Source ids shazeer-2017-sparsely-gated-moe, fedus-2021-switch-transformer, zhu-2025-megascale-infer must support the exact object, not just the surrounding topic. - The stable content-object key lets local drafts, prompts, and route memory attach without changing the source page. - The concept explanation is local atlas prose until checked against its math, code, and source support. - Prerequisite gaps should become a repair route, not a reason to leave the object vague. - Role-lens requests: - Learner: ask for "Ask what would make "MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism" feel predictable rather than familiar." | assumption: Source ids shazeer-2017-sparsely-gated-moe, fedus-2021-switch-transformer, zhu-2025-megascale-infer must support the exact object, not just the surrounding topic. | next action: The learner can state the mechanism in their own words - Researcher: ask for "Source ids to inspect: shazeer-2017-sparsely-gated-moe, fedus-2021-switch-transformer, zhu-2025-megascale-infer" | assumption: The stable content-object key lets local drafts, prompts, and route memory attach without changing the source page. | next action: The learner can name the prerequisite that would repair confusion - Experimenter: ask for "Choose one variable or condition to perturb before asking for an explanation." | assumption: The concept explanation is local atlas prose until checked against its math, code, and source support. | next action: The learner can predict how the mechanism changes under one perturbation - Professor: ask for "Find the smallest transferable rule a learner could reuse without the AI." | assumption: Prerequisite gaps should become a repair route, not a reason to leave the object vague. | next action: Teach or transfer: Turn the mechanism into one sentence that predicts a neighboring concept. What would resolve this: - The learner can state the mechanism in their own words - The learner can name the prerequisite that would repair confusion - The learner can predict how the mechanism changes under one perturbation Answer as a careful research tutor: stay source-grounded, separate verified evidence from assumptions, name the relevant math objects, and end with one next action. Current deterministic role lens for this object: - Role lens: Learner - Evidence request: Ask what would make "MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism" feel predictable rather than familiar. - Assumption to keep visible: Source ids shazeer-2017-sparsely-gated-moe, fedus-2021-switch-transformer, zhu-2025-megascale-infer must support the exact object, not just the surrounding topic. - Proposed experiment: Ask the learner to perturb one representation, then check whether the same invariant survives in math, code, and demo. - Next action: The learner can state the mechanism in their own words

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Speculative Decoding: Lossless Multi-Token Generation

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LLM Systems

llm-systems
moe
serving
scheduling
all-to-all

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Speculative Decoding: Lossless Multi-Token Generation

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You are my AI learning companion for Continuous Function. Current context: LLM Systems concept. Learning surface: MoE Serving & Scheduling: Token Dispatch, All-to-All, Disaggregated Parallelism. What this page says: Serving MoE turns sparse compute into a scheduling problem: routing skew can create stragglers and token-dispatch communication can bottleneck, motivating scheduling and, in systems such as MegaScale-Infer, disaggregated expert-parallel serving. Current section: Intuition, math, code, and interactive demo. Suggested next step: Manipulate one control and predict the visible change.. Learner goal: Understand the idea. Learner comfort level: New to this. Preferred explanation style: Visual first. Task: Explain the central idea in plain language, then restate it with the exact math objects from the page. Answer in a way that helps me learn: ask one clarifying question only if needed, use intuition before notation, and end with one thing I should try on the page.

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