vLLM/docs/features/disagg_encoder.md

Disaggregated Encoder

A disaggregated encoder runs the vision-encoder stage of a multimodal LLM in a process that is separate from the pre-fill / decoder stage. Deploying these two stages in independent vLLM instances brings three practical benefits:

1. Independent, fine-grained scaling
2. Lower time-to-first-token (TTFT)
3. Cross-process reuse and caching of encoder outputs

Design doc: https://docs.google.com/document/d/1aed8KtC6XkXtdoV87pWT0a8OJlZ-CpnuLLzmR8l9BAE

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1 Motivation

1. Independent, fine-grained scaling

• Vision encoders are lightweight, while language models are orders of magnitude larger.
• The language model can be parallelised without affecting the encoder fleet.
• Encoder nodes can be added or removed independently.

2. Lower time-to-first-token (TTFT)

• Language-only requests bypass the vision encoder entirely.
• Encoder output is injected only at required attention layers, shortening the pre-fill critical path.

3. Cross-process reuse and caching

• In-process encoders confine reuse to a single worker.
• A remote, shared cache lets any worker retrieve existing embeddings, eliminating redundant computation.

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2 Usage Example

ExampleConnector

The following scripts demonstrate the workflow with ExampleConnector:

1 Encoder instance + 1 PD instance: examples/disaggregated/disaggregated_encoder/disagg_1e1pd_example.sh

1 Encoder instance + 1 Prefill instance + 1 Decode instance: examples/disaggregated/disaggregated_encoder/disagg_1e1p1d_example.sh

ECMooncakeConnector

ECMooncakeConnector transfers encoder outputs using the Mooncake TransferEngine. See the Mooncake integration example for a complete 1 Encoder instance + 1 PD instance setup, including the producer and consumer --ec-transfer-config settings and proxy configuration.

With vLLM and Mooncake installed, run the example from the repository root:

GPU_E=0 GPU_PD=1 MOONCAKE_EC_PROTOCOL=tcp \
    bash tests/v1/ec_connector/integration/run_epd_mooncake_ec_full_pipeline.sh

The script uses Qwen/Qwen2.5-VL-3B-Instruct by default, runs a single-GPU baseline, and compares the disaggregated outputs against it. Set MODEL to use another model or MOONCAKE_EC_PROTOCOL=rdma to use RDMA on supported hardware. See the script for additional configuration options.

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3 Test Script

For optional cross-Encoder output reuse while retaining Mooncake P2P delivery, see Cross-encoder output reuse.

Please refer to the directories tests/v1/ec_connector

4 Development

Disaggregated encoding is implemented by running two parts:

• Encoder instance – a vLLM instance to performs vision encoding.
• Prefill/Decode (PD) instance(s) – runs language pre-fill and decode.
  • PD can be in either a single normal instance with disagg_encoder_example.sh (E->PD) or in disaggregated instances with disagg_epd_example.sh (E->P->D)

A connector transfers encoder-cache (EC) embeddings from the encoder instance to the PD instance.
All related code is under vllm/distributed/ec_transfer.

Key abstractions

• ECConnector – interface for retrieving EC caches produced by the encoder.
  • Scheduler role – checks cache existence and schedules loads.
  • Worker role – loads the embeddings into memory.

Here is a figure illustrating disaggregate encoder flow:

For the PD disaggregation part, the Prefill instance receives cache exactly the same as the disaggregated encoder flow above. Prefill instance executes 1 step (prefill -> 1 token output) and then transfers KV cache to the Decode instance for the remaining execution. The KV transfer part purely happens after the execution of the PD instance.

docs/features/disagg_prefill.md shows the brief idea about the disaggregated prefill (v0)

We create the example setup with the NixlConnector from vllm/distributed/kv_transfer/kv_connector/v1/nixl/ and referred to the tests/v1/kv_connector/nixl_integration/toy_proxy_server.py to facilitate the kv transfer between P and D;