Roles in DisaggregatedSet

A Role in DisaggregatedSet represents a distinct operational phase in a disaggregated serving architecture (e.g., prefill, decode, or encode). Each role defines its own pod specifications, scaling properties, and replica topology.

Relationship to Child LeaderWorkerSets

Each role defined in a DisaggregatedSet specification maps directly to an independent child LeaderWorkerSet managed by the DisaggregatedSet controller:

DisaggregatedSet "my-inference"
├── roles[0]: prefill  →  LeaderWorkerSet "my-inference-0-<rev>-prefill"
├── roles[1]: decode   →  LeaderWorkerSet "my-inference-0-<rev>-decode"
└── roles[2]: encode   →  LeaderWorkerSet "my-inference-0-<rev>-encode"

Child LeaderWorkerSets follow the naming convention: <DisaggregatedSet-name>-<slice>-<revision-hash>-<role-name>

Role Configuration Fields

A DisaggregatedSet spec defines a roles list where each entry represents a role (DisaggregatedRoleSpec):

Example Multi-Role Configuration

Here is an example DisaggregatedSet defining independent prefill and decode roles with different pod group sizes and hardware accelerator configurations:

apiVersion: disaggregatedset.x-k8s.io/v1
kind: DisaggregatedSet
metadata:
  name: disaggregatedset-sample
spec:
  roles:
  - name: prefill
    spec:
      replicas: 2
      leaderWorkerTemplate:
        size: 4
        workerTemplate:
          spec:
            containers:
            - name: vllm-prefill
              image: vllm/vllm-openai:latest
              resources:
                limits:
                  nvidia.com/gpu: "8"
  - name: decode
    spec:
      replicas: 4
      leaderWorkerTemplate:
        size: 2
        workerTemplate:
          spec:
            containers:
            - name: vllm-decode
              image: vllm/vllm-openai:latest
              resources:
                limits:
                  nvidia.com/gpu: "4"

Independent Per-Role Capabilities

Because each role maps to an independent child LeaderWorkerSet, each role inherits all core LWS features tailored to its specific workload phase:

1. Heterogeneous Hardware: Prefill servers can run on high-bandwidth, high-compute accelerator nodes (e.g., 8-GPU tensor-parallel groups), while decode servers run on memory-optimized nodes (e.g., 2-GPU or 4-GPU groups).
2. Independent Group Sizes: Each role configures its own leaderWorkerTemplate.size and optional subGroupPolicy.
3. Independent Autoscaling: Prefill and decode roles can be scaled dynamically based on distinct metric signals (e.g., time-to-first-token vs. inter-token-latency) using DisaggregatedSetRoleScaler.
4. Dedicated Storage: Each role can configure its own volumeClaimTemplates for local model caching or intermediate tensor offloading.