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Disaggregated Inference Serving in llm-d

Overview

This document describes the architecture and request lifecycle for enabling disaggregated inference execution in the llm-d Router. llm-d supports multiple disaggregation topologies:

  • EPD (no disaggregation) – a single node handles all three functions (encode, prefill, and decode). This is the default mode when no disaggregation is configured.
  • P/D (Prefill/Decode) – separates the prefill and decode stages onto different workers. This is functionally equivalent to EP/D, since prefill workers also handle encoding (multimodal processing) as part of the prefill stage.
  • E/PD (Encode/Prefill-Decode) – offloads multimodal encoding to dedicated workers while a single worker handles prefill and decode.
  • E/P/D (Encode/Prefill/Decode) – the full three-stage pipeline where each stage runs on a specialized worker.

Note

The Encode (E) stage is only relevant for requests with multimodal content (images, video, or audio). For text-only requests, the encode stage is skipped regardless of the configured topology.

Warning

Encode disaggregation (E/PD and E/P/D) is under active development in both vLLM and llm-d-router. The implementation described here is a proof of concept (PoC) and is subject to change.

All topologies are driven by the unified disagg-profile-handler plugin, which selects active stages based on configuration, the user request (e.g., presence of multimodal content), and the system status (e.g., KV-cache hit ratio on the selected decode pod). The architecture aims to improve flexibility, scalability, and performance by enabling separation of inference stages onto different workers.

Goals

  • Enable routing of encode, prefill, and decode to different workers
  • Maintain low latency and high throughput
  • Improve resource utilization by specializing pods for each stage
  • Support multimodal workloads by offloading encoding to dedicated workers
  • Align with GIE-compatible architectures for potential upstreaming

Key Components

Component Role
Encode Worker Handles multimodal encoding (images, video, audio) for E/PD and E/P/D
Prefill Worker Handles prefill stage using vLLM engine; in EP/D configuration, also handles encoding for multimodal requests
Decode Worker Handles decode stage and contains the sidecar for coordination
Sidecar (Decode) Orchestrates communication with encode/prefill workers and manages lifecycle
Envoy Proxy Accepts OpenAI-style requests and forwards them to EPP
EPP Endpoint Picker, makes scheduling decisions

Request Lifecycle

P/D (Prefill/Decode)

  1. User Request – Sent via OpenAI API to the Envoy Proxy
  2. EPP Scheduling Decision – The disagg-profile-handler runs stages in order:
    1. Decode: always runs first, selects a decode pod
    2. Prefill (optional): the PD decider evaluates prompt length and prefix-cache hit; if disaggregation is warranted, a prefill pod is selected
  3. Execution – Request lands on Decode Worker:
    • If x-prefiller-host-port header doesn't exist → runs both stages locally
    • If x-prefiller-host-port header exists → sidecar sends prefill to the selected Prefill Worker, then runs decode locally
  4. Response Flow – decode sidecar → Envoy → EPP → User

E/PD (Encode/Prefill-Decode)

For multimodal requests (images, video, audio), the encode stage can be disaggregated to dedicated workers:

  1. User Request – Multimodal request sent via OpenAI API
  2. EPP Scheduling Decision – The disagg-profile-handler runs stages in order:
    1. Decode: selects a decode pod
    2. Encode (optional): the encode decider checks for multimodal content; if present, an encode pod is selected
  3. Execution – Request lands on Decode Worker:
    • If encode was scheduled → sidecar sends encoding work to the selected Encode Worker(s) via the x-encoder-hosts-ports header
    • Encode Worker processes multimodal content and returns encoding metadata (embedding references)
    • Decode Worker reads embeddings via EC_Connector and runs prefill + decode locally
  4. Response Flow – decode sidecar → Envoy → EPP → User

E/P/D (Encode/Prefill/Decode)

The full three-stage pipeline combines both encode and prefill disaggregation:

  1. User Request – Multimodal request sent via OpenAI API
  2. EPP Scheduling Decision – The disagg-profile-handler runs all three stages in order:
    1. Decode: selects a decode pod
    2. Encode (optional): if multimodal content is detected, an encode pod is selected
    3. Prefill (optional): if the PD decider determines disaggregation is beneficial, a prefill pod is selected
  3. Execution – Request lands on Decode Worker:
    • If encode was scheduled → sidecar sends encoding work to the selected Encode Worker(s) via the x-encoder-hosts-ports header
    • Encode Worker processes multimodal content and returns encoding metadata (embedding references)
    • If prefill was scheduled → sidecar sends prefill to Prefill Worker via the x-prefiller-host-port header
    • Prefill Worker reads embeddings via EC_Connector and executes prefill operation
    • Decode Worker runs decode locally
  4. Response Flow – decode sidecar → Envoy → EPP → User

Architectural Details

P/D Sequence

sequenceDiagram
  participant C as Client
  participant I as Inference Gateway
  participant DS as Decode Worker Sidecar
  participant D as Decode Worker(vLLM)
  participant P as Prefill Worker(vLLM)

  C->>I: Inference Request
  I->>DS: Request is sent to the Decode Worker Sidecar <br/> with the selected Prefill worker set in a header.
  DS->>P: Remote Prefill with prompt(max_tokens=1)
  P-->>P: Run prefill
  P->>DS: Remote kv parameters
  DS->> D: Request is sent to the Decode Worker (vLLM) with remote_prefill true, <br/>prefill ID and memory block IDs
        D-->>P: Read kv-cache
        D-->>D: Schedule decode into queue & run decode
  D->>DS: Inference Response
  DS->>I: Inference Response
  I->>C: Inference Response
Loading

E/PD Sequence

sequenceDiagram
  participant C as Client
  participant I as Inference Gateway
  participant DS as Decode Worker Sidecar
  participant E as Encode Worker
  participant D as Decode Worker(vLLM)

  C->>I: Multimodal Inference Request
  I->>DS: Request with x-encoder-hosts-ports header
  DS->>E: Send multimodal content for encoding
  E-->>E: Process images/video/audio
  E->>DS: Encoding metadata (embedding references)
  DS->>D: Request with encoding metadata
  D-->>E: Read embeddings via EC_Connector
  D-->>D: Run prefill + decode locally
  D->>DS: Inference Response
  DS->>I: Inference Response
  I->>C: Inference Response
Loading

E/P/D Sequence

sequenceDiagram
  participant C as Client
  participant I as Inference Gateway
  participant DS as Decode Worker Sidecar
  participant E as Encode Worker
  participant P as Prefill Worker(vLLM)
  participant D as Decode Worker(vLLM)

  C->>I: Multimodal Inference Request
  I->>DS: Request with x-encoder-hosts-ports <br/> and x-prefiller-host-port headers
  DS->>E: Send multimodal content for encoding
  E-->>E: Process images/video/audio
  E->>DS: Encoding metadata (embedding references)
  DS->>P: Remote Prefill with prompt and encoding metadata (max_tokens=1)
  P-->>E: Read embeddings via EC_Connector
  P-->>P: Run prefill
  P->>DS: Remote kv parameters
  DS->>D: Request with remote_prefill true, <br/>prefill ID and memory block IDs
        D-->>P: Read kv-cache
        D-->>D: Schedule decode into queue & run decode
  D->>DS: Inference Response
  DS->>I: Inference Response
  I->>C: Inference Response
Loading

Sidecar Responsibilities (Decode Only)

  • Receives EPP metadata (decode pod, optional encode pod(s), optional prefill pod)
  • If encode endpoints are present, sends multimodal content to Encode Worker(s), waits for results and validates them
  • If prefill endpoint is present, sends prefill request to Prefill Worker, waits for results and validates them
  • Launches local decode job
  • Sends final response

Note

No sidecar or coordination logic is needed on the prefill or encode nodes.

Worker Selection Logic

  • Decode Worker: Prefer longest prefix match / kv-cache utilization (depends on available scorers) and low load
  • Prefill Worker: Same scoring criteria as decode
  • Encode Worker: Selected when multimodal content is detected in the request

Skip prefill when:

  • Prefix match / kv-cache hit is high
  • Prompt is very short

Skip encode when:

  • Request contains no multimodal content (text-only)
  • Encode decider rejects the request

Drawbacks & Limitations

  • Slight increase in TTFT for disaggregated P/D and E/P/D
  • Additional network hops for E/P/D (encode → prefill → decode)
  • Possibility of stranded memory on prefill crash
  • The need for timeout and retry logic

Design Benefits

  • Flexibility: Enables per-request specialization and resource balancing
  • Scalability: Clean separation of concerns for easier ops and tuning
  • Upstream-ready: Follows GIE-compatible request handling
  • Minimal Changes: Only decode node includes orchestration sidecar

Future Considerations

  • Cache coordination (we can talk about 3 different types of cache: KV-cache, embeddings, and multimedia content)
  • Pre-allocation of kv blocks in the decode node, push cache from the prefill to the decode worker during calculation
  • More sophisticated encode worker selection (e.g., load-aware scheduling, cache content, locality-aware placement)

Integrating External Prefill/Decode Workloads

The llm-d Router supports integration with external disaggregated encode/prefill/decode (E/P/D) workloads or other inference frameworks that follow the same E/P/D separation pattern but use different Kubernetes Pod labeling conventions.

Labeling Convention Flexibility

By default, llm-d uses the label key llm-d.ai/role with values:

  • "encode" → encode-only pods (multimodal encoding)
  • "prefill" → prefill-only pods
  • "decode" → decode-capable pods
  • "encode-prefill" → pods capable of both encode and prefill (EP/D or P/D)
  • "encode-decode" → pods capable of both encode and decode (E/PD, rare)
  • "prefill-decode" → pods capable of both prefill and decode
  • "encode-prefill-decode" → pods capable of all three stages
  • "both" → deprecated (use "prefill-decode" instead)

However, external systems may use alternative labels like:

role: encode
role: prefill
role: decode

To accommodate this without code changes, you can configure the EndpointPickerConfig to use the generic label-selector-filter plugin instead of the hardcoded encode-filter / prefill-filter / decode-filter.

Note

The previous filter type by-label is deprecated. Use label-selector-filter with standard Kubernetes label selector syntax instead.

Configuration Examples

P/D Configuration

Below is a minimal EndpointPickerConfig for P/D disaggregation using custom labels:

apiVersion: llm-d.ai/v1alpha1
kind: EndpointPickerConfig
plugins:
  # Prefill selection: match Pods with label role=prefill
  - type: label-selector-filter
    name: "prefill-pods"
    parameters:
      matchExpressions:
        - key: "role"
          operator: In
          values: ["prefill"]
  # Decode selection: match Pods with label role=decode
  - type: label-selector-filter
    name: "decode-pods"
    parameters:
      matchExpressions:
        - key: "role"
          operator: In
          values: ["decode"]
  - type: prefix-cache-scorer
    parameters:
      autoTune: false
      blockSizeTokens: 5
      maxPrefixBlocksToMatch: 256
      lruCapacityPerServer: 31250
  - type: max-score-picker
  - type: prefix-based-pd-decider
    parameters:
      nonCachedTokens: 8
  - type: disagg-profile-handler
    parameters:
      profiles:
        prefill: prefill
        decode: decode
      deciders:
        prefill: prefix-based-pd-decider
schedulingProfiles:
  - name: prefill
    plugins:
      - pluginRef: "prefill-pods"
      - pluginRef: "max-score-picker"
      - pluginRef: "prefix-cache-scorer"
  - name: decode
    plugins:
      - pluginRef: "decode-pods"
      - pluginRef: "max-score-picker"
      - pluginRef: "prefix-cache-scorer"

E/P/D Configuration

Below is an EndpointPickerConfig for full E/P/D disaggregation using custom labels:

apiVersion: llm-d.ai/v1alpha1
kind: EndpointPickerConfig
plugins:
  # Encoding selection: match Pods with label role=encode
  - type: label-selector-filter
    name: "encode-pods"
    parameters:
      matchExpressions:
        - key: "role"
          operator: In
          values: ["encode"]
  # Prefill selection: match Pods with label role=prefill
  - type: label-selector-filter
    name: "prefill-pods"
    parameters:
      matchExpressions:
        - key: "role"
          operator: In
          values: ["prefill"]
  # Decode selection: match Pods with label role=decode
  - type: label-selector-filter
    name: "decode-pods"
    parameters:
      matchExpressions:
        - key: "role"
          operator: In
          values: ["decode"]
  - type: prefix-cache-scorer
    parameters:
      autoTune: false
      blockSizeTokens: 5
      maxPrefixBlocksToMatch: 256
      lruCapacityPerServer: 31250
  - type: max-score-picker
  - type: always-disagg-multimodal-decider
  - type: prefix-based-pd-decider
    parameters:
      nonCachedTokens: 8
  - type: disagg-profile-handler
    parameters:
      profiles:
        encode: encode
        prefill: prefill
        decode: decode
      deciders:
        encode: always-disagg-multimodal-decider
        prefill: prefix-based-pd-decider
schedulingProfiles:
  - name: encode
    plugins:
      - pluginRef: "encode-pods"
  - name: prefill
    plugins:
      - pluginRef: "prefill-pods"
      - pluginRef: "max-score-picker"
      - pluginRef: "prefix-cache-scorer"
  - name: decode
    plugins:
      - pluginRef: "decode-pods"
      - pluginRef: "max-score-picker"
      - pluginRef: "prefix-cache-scorer"

Diagram

Disaggregated Encode/Prefill/Decode Architecture

TODO: add E/P/D diagram

Deciders

Deciders are handler plugins responsible for determining whether a disaggregated stage should be executed for a given request.

PD Deciders

PD deciders determine whether prefill should be offloaded to a separate worker, based on the properties of the request prompt.

Prefix-Based PD Decider

The prefix-based-pd-decider plugin makes the disaggregation decision according to the length of the non-cached suffix of the prompt relative to tokens already cached on the selected decode pod.

How It Works

  • Once a decode pod is selected, the decider checks how many tokens from the incoming prompt have already been sent to this pod

  • If the remaining non-cached suffix length is longer than the configured threshold (nonCachedTokens), disaggregation is triggered – the prefill will run remotely on a prefill pod, and decode locally on the decode pod

  • If the non-cached suffix is shorter or equal to the threshold, the full request runs locally on the decode worker without remote prefill

Configuration

- type: prefix-based-pd-decider
  parameters:
    nonCachedTokens: 8

Parameter:

  • nonCachedTokens: Number of non-cached tokens that trigger disaggregation
    • If set to 0, disaggregation never occurs for any request

Always-Disagg PD Decider

The always-disagg-pd-decider is a simpler alternative used mainly for testing or benchmarking. It always triggers disaggregation, regardless of prefix cache state or prompt characteristics.

Configuration example:

- type: always-disagg-pd-decider

Note

This plugin accepts no parameters.

It’s useful for validating end-to-end prefill/decode splitting and comparing system performance under forced disaggregation.

Encode Deciders

Encode deciders determine whether multimodal encoding should be offloaded to dedicated encode workers.

Always Disagg Multimodal Decider

The always-disagg-multimodal-decider triggers encode disaggregation whenever the request contains multimodal content (images, video, or audio). Text-only requests are never sent to encode workers.

Configuration example:

- type: always-disagg-multimodal-decider

Note

This plugin accepts no parameters.

It checks for the presence of image_url, audio_url, video_url, or input_audio content blocks in the chat-completions request body. If any multimodal content is found, the encode stage is activated.

Profile Handler Configuration

The disagg-profile-handler plugin is the entry point for all disaggregation topologies. Active stages are determined by which deciders are configured.

Parameters

  • profiles (optional): names of the scheduling profiles to use.
    • decode (default: decode)
    • prefill (default: prefill)
    • encode (default: encode)
  • deciders (optional): decider plugins that control whether each stage runs.
    • prefill: enables P/D disaggregation when set.
    • encode: enables E disaggregation when set.

Examples

Decode-only (no disaggregation)

No deciders are configured -- all requests are handled by the decode profile alone.

- type: disagg-profile-handler

P/D (Prefill/Decode)

- type: disagg-profile-handler
  parameters:
    deciders:
      prefill: prefix-based-pd-decider

Custom profile names (if your scheduling profiles are not named decode/prefill):

- type: disagg-profile-handler
  parameters:
    profiles:
      decode: my-decode
      prefill: my-prefill
    deciders:
      prefill: prefix-based-pd-decider

E/PD (Encode/Prefill-Decode)

- type: disagg-profile-handler
  parameters:
    deciders:
      encode: always-disagg-multimodal-decider

E/P/D (Encode/Prefill/Decode)

- type: disagg-profile-handler
  parameters:
    deciders:
      prefill: prefix-based-pd-decider
      encode: always-disagg-multimodal-decider

Sidecar Configuration

The decode sidecar proxy is responsible for coordinating KV cache transfers between vLLM instances during disaggregated inference. It must be configured with the correct connector protocol matching the vLLM kv_connector used on the serving pods.

KV Connector (--kv-connector)

Specifies which KV transfer protocol the sidecar uses to coordinate prefill/decode disaggregation. This flag corresponds to the vLLM-side kv_connector value set in --kv-transfer-config on the serving pods, but uses its own naming convention.

--kv-connector value vLLM kv_connector Description
nixlv2 (default) NixlConnector NIXL-based KV transfer using RDMA/GPU-direct
shared-storage SharedStorageConnector KV transfer via shared filesystem
sglang — SGLang disaggregation protocol
mooncake MooncakeConnector Mooncake KV transfer using RDMA

General Sidecar Flags

Flag Env var Values Default Description
--enable-tls — prefiller, decoder, encoder (comma-separated or repeated) none Enable TLS for the specified stages. Example: --enable-tls=prefiller,decoder
--tls-insecure-skip-verify — prefiller, decoder, encoder (comma-separated or repeated) none Skip TLS certificate verification for the specified stages. Example: --tls-insecure-skip-verify=prefiller
--enable-prefiller-sampling ENABLE_PREFILLER_SAMPLING true / false false If true, the prefill instance is selected randomly from the provided prefill host values.
--enable-ssrf-protection — true / false false Enable SSRF protection using InferencePool allowlisting.

Connector-Specific Flags

Connector Flag Env var Default Description
mooncake --mooncake-bootstrap-port MOONCAKE_BOOTSTRAP_PORT 8998 Port used to query the Mooncake bootstrap endpoint on prefill pods. Corresponds to vLLM's VLLM_MOONCAKE_BOOTSTRAP_PORT.
sglang — SGLANG_BOOTSTRAP_PORT 8998 Port used for the SGLang bootstrap endpoint on prefill pods.

References