SGLang Multimodal#

This document provides a comprehensive guide for multimodal inference using SGLang backend in Dynamo. SGLang multimodal uses specialized E/PD or E/P/D flows with NIXL (RDMA) for zero-copy tensor transfer.

Support Matrix#

Modality

Input Format

Aggregated

Disaggregated

Notes

Image

HTTP/HTTPS URL

Yes

Yes

Vision encoder generates embeddings

Image

Data URL (Base64)

No

No

Video

HTTP/HTTPS URL

No

No

Audio

HTTP/HTTPS URL

No

No

Supported URL Formats#

Format

Example

Description

HTTP/HTTPS

http://example.com/image.jpg

Remote media files

Deployment Patterns#

SGLang supports E/PD and E/P/D patterns only (always has a separate encode worker). See Multimodal Architecture Patterns for detailed explanations.

Pattern

Supported

Launch Script

Notes

EPD (Simple Aggregated)

❌

N/A

Not supported

E/PD (Encode Separate)

✅

multimodal_agg.sh

Vision encoder separate

E/P/D (Full Disaggregation)

✅

multimodal_disagg.sh

KV cache via bootstrap

EP/D (Traditional Disaggregated)

❌

N/A

Not supported

Component Flags#

Component

Flag

Purpose

Processor

--multimodal-processor

HTTP entry, OpenAI→SGLang conversion

Encode Worker

--multimodal-encode-worker

Vision encoder, embeddings generation

PD Worker

--multimodal-worker

Prefill + Decode with embeddings

Decode Worker

--multimodal-worker --serving-mode=decode

Entry point for disaggregation

Prefill Worker

--multimodal-worker --serving-mode=prefill

Called by Decode, bootstrap coordination

SGLang-Specific Characteristics#

  • Vision Encoder in Python: Encode worker loads vision model (AutoModel) and image processor (AutoImageProcessor)

  • Token Expansion: Single <|image_pad|> token replaced with N tokens based on embedding shape

  • NIXL Transfer: Embeddings transferred from Encoder → PD Worker using NIXL

  • No Rust Processing: All tokenization and image handling happens in Python

Use the Latest Release#

We recommend using the latest stable release of dynamo to avoid breaking changes:

GitHub Release

You can find the latest release and check out the corresponding branch with:

git checkout $(git describe --tags $(git rev-list --tags --max-count=1))

E/PD Serving (Encode Separate)#

Components#

Workflow#

The MultimodalEncodeWorker downloads and encodes the image and passes the embeddings to the MultimodalWorker. The work complete event is sent via NATS, while the embeddings tensor is transferred via RDMA through the NIXL interface. The MultimodalWorker then prefills and decodes the prompt in the same engine, as in the LLM aggregated serving example. Only the processor is registered to the Dynamo frontend as an available endpoint. Workers do NOT register - they are internal components and communicate via NATS.

        flowchart LR
  HTTP --> processor
  processor --tokenized request + image_url--> encode_worker
  encode_worker --request + embeddings--> worker

  worker -.-> encode_worker
  encode_worker -.-> processor
  processor -.-> HTTP

Launch#

cd $DYNAMO_HOME/examples/backends/sglang
./launch/multimodal_agg.sh

Client:

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen2.5-VL-7B-Instruct",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "Describe the image."
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "http://images.cocodataset.org/test2017/000000155781.jpg"
            }
          }
        ]
      }
    ],
    "max_tokens": 50,
    "stream": false
  }' | jq

E/P/D Serving (Full Disaggregation)#

Components#

Workflow#

In models like Qwen2.5-VL, embeddings are only required during the prefill stage. The image embeddings are transferred via NIXL from the Encode Worker to the Decode Worker (the entry point for disaggregation), which then coordinates with the Prefill Worker. The Prefill Worker processes the embeddings and forwards the KV cache back to the Decode Worker for token generation.

        flowchart LR
  HTTP --> processor
  processor --tokenized request + image_url--> encode_worker
  encode_worker --request + embeddings--> worker
  worker --request + embeddings--> prefill_worker

  prefill_worker --KV Cache--> worker
  encode_worker -.-> processor
  worker -.-> encode_worker
  processor -.-> HTTP

Launch#

cd $DYNAMO_HOME/examples/backends/sglang
./launch/multimodal_disagg.sh

Client:

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen2.5-VL-7B-Instruct",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "Describe the image."
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "http://images.cocodataset.org/test2017/000000155781.jpg"
            }
          }
        ]
      }
    ],
    "max_tokens": 50,
    "stream": false
  }' | jq

Bootstrap Coordination#

SGLang disaggregation uses a bootstrap mechanism for P->D coordination:

Request Flow (Important)#

Client → Frontend → Processor → Encode → DECODE Worker → Prefill Worker
                                               ↑
                                    Entry point for disaggregation!

Bootstrap Process#

  1. Decode Worker receives request from Encode Worker

  2. Decode Worker calls Prefill Worker via NATS to request bootstrap info

  3. Prefill Worker generates {host, port, room} and returns immediately

  4. Both workers connect to same “room” using bootstrap coordinates

  5. SGLang internally transfers KV cache state via bootstrap connection (not NIXL)

Key Difference from vLLM#

  • vLLM: Frontend → Prefill → Decode (Prefill is entry point)

  • SGLang: Frontend → Processor → Encode → Decode → Prefill (Decode is entry point)

Inter-Component Communication#

Control Flow (NATS)#

All component-to-component communication happens via NATS:

E/PD Mode (Encode Separate)#

Processor → Encode Worker → PD Worker
  (NATS)        (NATS + NIXL embeddings)

E/P/D Mode (Full Disaggregation)#

Processor → Encode Worker → DECODE Worker → Prefill Worker
  (NATS)        (NATS)            (NATS)
                             ↓
                    Decode requests bootstrap
                             ↓
                    Prefill returns {host, port, room}
                             ↓
                    Both connect via bootstrap
                             ↓
                    SGLang internal KV cache transfer

Detailed Message Flow#

Processor → Encode Worker:
  - NATS round_robin with SglangMultimodalRequest
  - Contains: tokenized input_ids, image URL, sampling params

Encode Worker → Decode/PD Worker:
  - NATS round_robin to "backend" component
  - Contains: expanded token_ids, NIXL metadata, embeddings shape
  - NIXL transfer: embeddings tensor

Decode Worker → Prefill Worker (disagg only):
  - NATS call to "prefill" component
  - Decode requests bootstrap coordinates
  - Prefill returns: {bootstrap_host, bootstrap_port, bootstrap_room}

Prefill ↔ Decode (via bootstrap):
  - SGLang internal connection (not NATS)
  - KV cache state shared via bootstrap mechanism

Data Transfer (NIXL)#

NIXL is used only for embedding transfer:

# Encode Worker
descriptor = connect.Descriptor(precomputed_embeddings)
with connector.create_readable(descriptor) as readable:
    request.serialized_request = readable.metadata()
    await pd_worker_client.round_robin(request)
    await readable.wait_for_completion()

# PD Worker
embeddings = torch.empty(request.embeddings_shape, dtype=torch.float16)
descriptor = connect.Descriptor(embeddings)
read_op = await connector.begin_read(request.serialized_request, descriptor)
await read_op.wait_for_completion()

Vision Encoding Details#

Encode Worker Components#

The encode worker loads and runs the vision model in Python:

self.image_processor = AutoImageProcessor.from_pretrained(
    model_path, trust_remote_code=True
)
self.vision_model = AutoModel.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype=torch.float16,
    trust_remote_code=True
)

Token Expansion Process#

  1. Processor inserts single image token (e.g., <|image_pad|>)

  2. Encode worker generates embeddings: shape = (batch, num_patches, hidden_dim)

  3. Encode worker replaces single token with num_patches tokens

  4. Downstream worker receives expanded token sequence

Example:

# Before: ["Hello", "<|image_pad|>", "world"]
# After:  ["Hello", "<|image_pad|>", "<|image_pad|>", ...(576 tokens), "world"]

Chat Template Processing#

SGLang uses its own chat template system:

from sglang.srt.parser.conversation import chat_templates

conv = chat_templates["qwen2-vl"].copy()
conv.append_message(conv.roles[0], f"{conv.image_token} Describe this image")
processed = tokenizer(text=conv.get_prompt(), return_tensors="pt")

Supported templates: qwen2-vl, llama-3, vicuna, etc.

NIXL Usage#

Use Case

NIXL Used?

Data Transfer

Notes

E/PD (Encode Separate)

Yes

Encoder → PD (embeddings)

Vision encoder separate

E/P/D (Full Disaggregation)

Yes

Encoder → Prefill (embeddings)

KV cache via SGLang bootstrap

Key Difference: SGLang P/D uses bootstrap mechanism, not NIXL for KV cache like vLLM.

Known Limitations#

  • No Data URL support - Only HTTP/HTTPS URLs supported; data:image/... base64 URLs not supported

  • No pre-computed embeddings - Cannot use .pt, .pth, .bin embedding files; vision encoder runs for every request

  • No video support - No video encoder implementation

  • No audio support - No audio encoder implementation

  • Only Processor registers with Dynamo - Workers are internal components, frontend routes to Processor only

  • Disaggregated routing - Decode Worker is the entry point (calls Prefill), cannot route directly to Prefill workers

  • Limited model generalization - Token expansion logic is model-specific; adding new models may require implementation updates

Supported Models#

SGLang multimodal only supports image-based vision-language models:

  • Qwen2-VL / Qwen2.5-VL (primary support)

  • Models with AutoImageProcessor and vision tower

  • Models compatible with SGLang’s image embedding format

Key Files#

File

Description

components/src/dynamo/sglang/main.py

Component initialization, only Processor registers

components/src/dynamo/sglang/request_handlers/multimodal/processor_handler.py

Processor implementation, OpenAI→SGLang

components/src/dynamo/sglang/request_handlers/multimodal/encode_worker_handler.py

Vision encoder, embeddings generation

components/src/dynamo/sglang/request_handlers/multimodal/worker_handler.py

PD/Prefill/Decode workers, NIXL read

components/src/dynamo/sglang/multimodal_utils/multimodal_chat_processor.py

Chat template processing

components/src/dynamo/sglang/protocol.py

Request/response data structures

components/src/dynamo/sglang/register.py

Registration logic (only called for Processor)
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