feat: Add multimodal EPD for Sglang (#3230)

Signed-off-by: krishung5 <krish@nvidia.com>

components/backends/sglang/README.md

@@ -39,6 +39,7 @@ git checkout $(git describe --tags $(git rev-list --tags --max-count=1))
 | [**Conditional Disaggregation**](../../../docs/architecture/disagg_serving.md#conditional-disaggregation) | 🚧 | WIP [PR](https://github.com/sgl-project/sglang/pull/7730) |
 | [**KV-Aware Routing**](../../../docs/architecture/kv_cache_routing.md) | ✅ |  |
 | [**SLA-Based Planner**](../../../docs/architecture/sla_planner.md) | ✅ |  |
+| [**Multimodal EPD Disaggregation**](docs/multimodal_epd.md) | ✅ |  |
 | [**Load Based Planner**](../../../docs/architecture/load_planner.md) | ❌ | Planned |
 | [**KVBM**](../../../docs/architecture/kvbm_architecture.md) | ❌ | Planned |
 
@@ -254,6 +255,9 @@ Below we provide a selected list of advanced examples. Please open up an issue i
 ### Hierarchical Cache (HiCache)
 - **[Enable SGLang Hierarchical Cache (HiCache)](docs/sgl-hicache-example.md)**
 
+### Multimodal Encode-Prefill-Decode (EPD) Disaggregation with NIXL
+- **[Run a multimodal model with EPD Disaggregation](docs/multimodal_epd.md)**
+
 ## Deployment
 
 We currently provide deployment examples for Kubernetes and SLURM.

components/backends/sglang/docs/multimodal_epd.md

+<!--
+SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+SPDX-License-Identifier: Apache-2.0
+-->
+
+# Encode-Prefill-Decode (EPD) Flow with NIXL
+
+For high-performance multimodal inference with large embeddings, Dynamo supports a specialized **Encode-Prefill-Decode (EPD)** flow using **NIXL (RDMA)** for zero-copy tensor transfer.
+
+## Use the Latest Release
+
+We recommend using the latest stable release of dynamo to avoid breaking changes:
+
+[![GitHub Release](https://img.shields.io/github/v/release/ai-dynamo/dynamo)](https://github.com/ai-dynamo/dynamo/releases/latest)
+
+You can find the latest release [here](https://github.com/ai-dynamo/dynamo/releases/latest) and check out the corresponding branch with:
+
+```bash
+git checkout $(git describe --tags $(git rev-list --tags --max-count=1))
+```
+
+## Multimodal Aggregated Serving
+
+### Components
+
+- workers: For aggregated serving, we have two workers, [MultimodalEncodeWorker](src/dynamo/sglang/request_handlers/multimodal_encode_worker_handler.py) for encoding and [MultimodalWorker](src/dynamo/sglang/request_handlers/multimodal_worker_handler.py) for prefilling and decoding.
+- processor: Tokenizes the prompt and passes it to the MultimodalEncodeWorker.
+
+### Workflow
+
+
+The MultimodalEncodeWorker is responsible for encoding the image and passing the embeddings to the MultimodalWorker via a combination of NATS and RDMA.
+The work complete event is sent via NATS, while the embeddings tensor is transferred via RDMA through the NIXL interface.
+Its MultimodalWorker then prefills and decodes the prompt, just like the [LLM aggregated serving](../README.md) example.
+By separating the encode from the prefill and decode stages, we can have a more flexible deployment and scale the
+MultimodalEncodeWorker independently from the prefill and decode workers if needed.
+
+This figure illustrates the workflow:
+```mermaid
+flowchart LR
+  HTTP --> processor
+  processor --> HTTP
+  processor --image_url--> encode_worker
+  encode_worker --> processor
+  encode_worker --embeddings descriptor--> worker
+  worker --> encode_worker
+```
+
+```bash
+cd $DYNAMO_HOME/components/backends/sglang
+./launch/multimodal_agg.sh
+```
+
+### Client
+
+In another terminal:
+```bash
+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
+```
+
+You should see a response similar to this:
+```json
+{
+  "id": "chatcmpl-2546f44756884a14916ce13ebaa09da8",
+  "choices": [
+    {
+      "index": 0,
+      "message": {
+        "content": "This image shows a public transit bus on a dimly lit, street-level track in what appears to be a quiet urban neighborhood or suburban area. The bus displays \"OUT OF SERVICE\" in red on its illuminated sign. It is positioned",
+        "role": "assistant",
+        "reasoning_content": null
+      },
+      "finish_reason": "length"
+    }
+  ],
+  "created": 1758824222,
+  "model": "Qwen/Qwen2.5-VL-7B-Instruct",
+  "object": "chat.completion",
+  "usage": {
+    "prompt_tokens": 0,
+    "completion_tokens": 40,
+    "total_tokens": 40
+  }
+}
+```
+
+## Multimodal Disaggregated Serving
+
+### Components
+
+- workers: For disaggregated serving, we have three workers, [MultimodalEncodeWorker](src/dynamo/sglang/request_handlers/multimodal_encode_worker_handler.py) for encoding, [MultimodalWorker](src/dynamo/sglang/request_handlers/multimodal_worker_handler.py) for decoding, and [MultimodalPrefillWorker](src/dynamo/sglang/request_handlers/multimodal_worker_handler.py) for prefilling.
+- processor: Tokenizes the prompt and passes it to the MultimodalEncodeWorker.
+
+### Workflow
+
+For the Qwen2.5-VL model, embeddings are only required during the prefill stage. As such, the image embeddings are transferred using a NIXL descriptor from the encode worker to the worker and then passed to the prefill worker for processing.
+The prefill worker performs the prefilling step and forwards the KV cache to the worker for decoding.
+For more details on the roles of the prefill and decode workers, refer to the [LLM disaggregated serving](../README.md) example.
+
+This figure illustrates the workflow:
+```mermaid
+flowchart LR
+  HTTP --> processor
+  processor --> HTTP
+  processor --image_url--> encode_worker
+  encode_worker --> processor
+  encode_worker --embeddings descriptor--> worker
+  worker --> encode_worker
+  worker --embeddings descriptor--> prefill_worker
+  prefill_worker --> worker
+```
+
+
+```bash
+cd $DYNAMO_HOME/components/backends/sglang
+./launch/multimodal_disagg.sh
+```
+
+### Client
+
+In another terminal:
+```bash
+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
+```
+
+You should see a response similar to this:
+```json
+{
+  "id": "chatcmpl-2546f44756884a14916ce13ebaa09da8",
+  "choices": [
+    {
+      "index": 0,
+      "message": {
+        "content": "This image shows a public transit bus on a dimly lit, street-level track in what appears to be a quiet urban neighborhood or suburban area. The bus displays \"OUT OF SERVICE\" in red on its illuminated sign. It is positioned",
+        "role": "assistant",
+        "reasoning_content": null
+      },
+      "finish_reason": "length"
+    }
+  ],
+  "created": 1758824222,
+  "model": "Qwen/Qwen2.5-VL-7B-Instruct",
+  "object": "chat.completion",
+  "usage": {
+    "prompt_tokens": 0,
+    "completion_tokens": 40,
+    "total_tokens": 40
+  }
+}
+```

components/backends/sglang/launch/multimodal_agg.sh

+#!/bin/bash
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+set -e
+trap 'echo Cleaning up...; kill 0' EXIT
+
+# Default values
+MODEL_NAME="Qwen/Qwen2.5-VL-7B-Instruct"
+CHAT_TEMPLATE="qwen2-vl"
+PROVIDED_CHAT_TEMPLATE=""
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --model)
+            MODEL_NAME=$2
+            shift 2
+            ;;
+        --chat-template)
+            PROVIDED_CHAT_TEMPLATE=$2
+            shift 2
+            ;;
+        -h|--help)
+            echo "Usage: $0 [OPTIONS]"
+            echo "Options:"
+            echo "  --model <model_name> Specify the model to use (default: $MODEL_NAME)"
+            echo "  --chat-template <template> Specify the SGLang chat template to use (default: $CHAT_TEMPLATE)"
+            echo "  -h, --help           Show this help message"
+            exit 0
+            ;;
+        *)
+            echo "Unknown option: $1"
+            echo "Use --help for usage information"
+            exit 1
+            ;;
+    esac
+done
+
+# Set CHAT_TEMPLATE if provided
+if [[ -n "$PROVIDED_CHAT_TEMPLATE" ]]; then
+    CHAT_TEMPLATE="$PROVIDED_CHAT_TEMPLATE"
+fi
+
+# Get the directory where this script is located
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+SGLANG_BACKEND_DIR="$SCRIPT_DIR/src"
+
+# run ingress
+python -m dynamo.frontend --http-port=8000 &
+
+# run SGLang multimodal processor
+python3 -m dynamo.sglang --multimodal-processor --model-path "$MODEL_NAME" --chat-template "$CHAT_TEMPLATE" &
+
+# run SGLang multimodal encode worker
+CUDA_VISIBLE_DEVICES=0 python3 -m dynamo.sglang --multimodal-encode-worker --model-path "$MODEL_NAME" --chat-template "$CHAT_TEMPLATE" &
+
+# run SGLang multimodal inference worker
+CUDA_VISIBLE_DEVICES=1 python3 -m dynamo.sglang \
+  --multimodal-worker \
+  --model-path "$MODEL_NAME" \
+  --page-size 16 \
+  --tp 1 \
+  --trust-remote-code \
+  --skip-tokenizer-init \
+  --disaggregation-transfer-backend nixl &
+
+# Wait for all background processes to complete
+wait

components/backends/sglang/launch/multimodal_disagg.sh

+#!/bin/bash
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+set -e
+trap 'echo Cleaning up...; kill 0' EXIT
+
+# Default values
+MODEL_NAME="Qwen/Qwen2.5-VL-7B-Instruct"
+CHAT_TEMPLATE="qwen2-vl"
+PROVIDED_CHAT_TEMPLATE=""
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --model)
+            MODEL_NAME=$2
+            shift 2
+            ;;
+        --chat-template)
+            PROVIDED_CHAT_TEMPLATE=$2
+            shift 2
+            ;;
+        -h|--help)
+            echo "Usage: $0 [OPTIONS]"
+            echo "Options:"
+            echo "  --model <model_name> Specify the model to use (default: $MODEL_NAME)"
+            echo "  --chat-template <template> Specify the SGLang chat template to use (default: $CHAT_TEMPLATE)"
+            echo "  -h, --help           Show this help message"
+            exit 0
+            ;;
+        *)
+            echo "Unknown option: $1"
+            echo "Use --help for usage information"
+            exit 1
+            ;;
+    esac
+done
+
+# Set CHAT_TEMPLATE if provided
+if [[ -n "$PROVIDED_CHAT_TEMPLATE" ]]; then
+    CHAT_TEMPLATE="$PROVIDED_CHAT_TEMPLATE"
+fi
+
+# Get the directory where this script is located
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+SGLANG_BACKEND_DIR="$SCRIPT_DIR/src"
+
+# run ingress
+python -m dynamo.frontend --http-port=8000 &
+
+# run SGLang multimodal processor
+python3 -m dynamo.sglang --multimodal-processor --model-path "$MODEL_NAME" --chat-template "$CHAT_TEMPLATE" &
+
+# run SGLang multimodal encode worker
+CUDA_VISIBLE_DEVICES=0 python3 -m dynamo.sglang --multimodal-encode-worker --model-path "$MODEL_NAME" --chat-template "$CHAT_TEMPLATE" &
+
+# run SGLang multimodal prefill worker
+CUDA_VISIBLE_DEVICES=1 python3 -m dynamo.sglang \
+  --multimodal-worker \
+  --model-path "$MODEL_NAME" \
+  --page-size 16 \
+  --tp 1 \
+  --trust-remote-code \
+  --skip-tokenizer-init \
+  --disaggregation-mode prefill \
+  --disaggregation-transfer-backend nixl &
+
+# run SGLang multimodal decode worker
+CUDA_VISIBLE_DEVICES=2 python3 -m dynamo.sglang \
+  --multimodal-worker \
+  --model-path "$MODEL_NAME" \
+  --page-size 16 \
+  --tp 1 \
+  --trust-remote-code \
+  --skip-tokenizer-init \
+  --disaggregation-mode decode \
+  --disaggregation-transfer-backend nixl &
+
+# Wait for all background processes to complete
+wait

components/src/dynamo/sglang/args.py

@@ -10,7 +10,7 @@ import sys
 from argparse import Namespace
 from dataclasses import dataclass
 from enum import Enum
-from typing import Any, Dict, Optional
+from typing import Any, Dict, List, Optional
 
 from sglang.srt.server_args import ServerArgs
 
@@ -61,6 +61,24 @@ DYNAMO_ARGS: Dict[str, Dict[str, Any]] = {
         "default": False,
         "help": "Use SGLang's tokenizer. This will skip tokenization of the input and output and only v1/chat/completions will be available when using the dynamo frontend. Cannot be used with --custom-jinja-template.",
     },
+    "multimodal-processor": {
+        "flags": ["--multimodal-processor"],
+        "action": "store_true",
+        "default": False,
+        "help": "Run as multimodal processor component for handling multimodal requests",
+    },
+    "multimodal-encode-worker": {
+        "flags": ["--multimodal-encode-worker"],
+        "action": "store_true",
+        "default": False,
+        "help": "Run as multimodal encode worker component for processing images/videos",
+    },
+    "multimodal-worker": {
+        "flags": ["--multimodal-worker"],
+        "action": "store_true",
+        "default": False,
+        "help": "Run as multimodal worker component for LLM inference with multimodal data",
+    },
 }
 
 
@@ -79,6 +97,11 @@ class DynamoArgs:
     # preprocessing options
     use_sglang_tokenizer: bool = False
 
+    # multimodal options
+    multimodal_processor: bool = False
+    multimodal_encode_worker: bool = False
+    multimodal_worker: bool = False
+
 
 class DisaggregationMode(Enum):
     AGGREGATED = "agg"
@@ -99,6 +122,8 @@ class Config:
             return DisaggregationMode.PREFILL
         elif self.server_args.disaggregation_mode == "decode":
             return DisaggregationMode.DECODE
+        else:
+            return DisaggregationMode.AGGREGATED
 
 
 def _set_parser(
@@ -180,6 +205,15 @@ def parse_args(args: list[str]) -> Config:
             and parsed_args.disaggregation_mode == "prefill"
         ):
             endpoint = f"dyn://{namespace}.prefill.generate"
+        elif parsed_args.multimodal_processor:
+            endpoint = f"dyn://{namespace}.processor.generate"
+        elif parsed_args.multimodal_encode_worker:
+            endpoint = f"dyn://{namespace}.encoder.generate"
+        elif (
+            parsed_args.multimodal_worker
+            and parsed_args.disaggregation_mode == "prefill"
+        ):
+            endpoint = f"dyn://{namespace}.prefill.generate"
         else:
             endpoint = f"dyn://{namespace}.backend.generate"
 
@@ -231,6 +265,9 @@ def parse_args(args: list[str]) -> Config:
         reasoning_parser=reasoning_parser,
         custom_jinja_template=expanded_template_path,
         use_sglang_tokenizer=parsed_args.use_sglang_tokenizer,
+        multimodal_processor=parsed_args.multimodal_processor,
+        multimodal_encode_worker=parsed_args.multimodal_encode_worker,
+        multimodal_worker=parsed_args.multimodal_worker,
     )
     logging.debug(f"Dynamo args: {dynamo_args}")
 
@@ -264,6 +301,21 @@ def reserve_free_port(host: str = "localhost"):
         sock.close()
 
 
+def parse_endpoint(endpoint: str) -> List[str]:
+    """Parse endpoint string into namespace, component, and endpoint parts."""
+    endpoint_str = endpoint.replace("dyn://", "", 1)
+    endpoint_parts = endpoint_str.split(".")
+    if len(endpoint_parts) != 3:
+        error_msg = (
+            f"Invalid endpoint format: '{endpoint}'. "
+            f"Expected 'dyn://namespace.component.endpoint' or 'namespace.component.endpoint'."
+        )
+        logging.error(error_msg)
+        raise ValueError(error_msg)
+
+    return endpoint_parts
+
+
 def _reserve_disaggregation_bootstrap_port():
     """
     Each worker requires a unique port for disaggregation_bootstrap_port.

components/src/dynamo/sglang/main.py

@@ -11,17 +11,24 @@ import sglang as sgl
 import uvloop
 from sglang.srt.utils import get_ip
 
-from dynamo.llm import ZmqKvEventPublisher, ZmqKvEventPublisherConfig
+from dynamo.llm import ModelInput, ZmqKvEventPublisher, ZmqKvEventPublisherConfig
 from dynamo.runtime import DistributedRuntime, dynamo_worker
 from dynamo.runtime.logging import configure_dynamo_logging
-from dynamo.sglang.args import Config, DisaggregationMode, parse_args
+from dynamo.sglang.args import Config, DisaggregationMode, parse_args, parse_endpoint
 from dynamo.sglang.health_check import (
     SglangHealthCheckPayload,
     SglangPrefillHealthCheckPayload,
 )
 from dynamo.sglang.publisher import setup_sgl_metrics
 from dynamo.sglang.register import register_llm_with_runtime_config
-from dynamo.sglang.request_handlers import DecodeWorkerHandler, PrefillWorkerHandler
+from dynamo.sglang.request_handlers import (
+    DecodeWorkerHandler,
+    MultimodalEncodeWorkerHandler,
+    MultimodalPrefillWorkerHandler,
+    MultimodalProcessorHandler,
+    MultimodalWorkerHandler,
+    PrefillWorkerHandler,
+)
 
 configure_dynamo_logging()
 
@@ -39,7 +46,16 @@ async def worker(runtime: DistributedRuntime):
     logging.info("Signal handlers will trigger a graceful shutdown of the runtime")
 
     config = parse_args(sys.argv[1:])
+    if config.dynamo_args.multimodal_processor:
+        await init_multimodal_processor(runtime, config)
+    elif config.dynamo_args.multimodal_encode_worker:
+        await init_multimodal_encode_worker(runtime, config)
+    elif config.dynamo_args.multimodal_worker:
         if config.serving_mode != DisaggregationMode.PREFILL:
+            await init_multimodal_worker(runtime, config)
+        else:
+            await init_multimodal_prefill_worker(runtime, config)
+    elif config.serving_mode != DisaggregationMode.PREFILL:
         await init(runtime, config)
     else:
         await init_prefill(runtime, config)
@@ -88,12 +104,6 @@ async def init(runtime: DistributedRuntime, config: Config):
     # Readiness gate: requests wait until model is registered
     ready_event = asyncio.Event()
 
-    async def gated_generate(request):
-        """Queue requests until model registration completes"""
-        await ready_event.wait()  # Block until model is ready
-        async for response in handler.generate(request):
-            yield response
-
     handler = DecodeWorkerHandler(
         component, engine, config, publisher, kv_publisher, prefill_client
     )
@@ -177,6 +187,202 @@ async def init_prefill(runtime: DistributedRuntime, config: Config):
         handler.cleanup()
 
 
+async def init_multimodal_processor(runtime: DistributedRuntime, config: Config):
+    """Initialize multimodal processor component"""
+    server_args, dynamo_args = config.server_args, config.dynamo_args
+    component = runtime.namespace(dynamo_args.namespace).component(
+        dynamo_args.component
+    )
+    await component.create_service()
+
+    generate_endpoint = component.endpoint(dynamo_args.endpoint)
+
+    # For processor, we need to connect to the encode worker
+    # Default endpoint for encode worker
+    encode_endpoint = f"dyn://{dynamo_args.namespace}.encoder.generate"
+    parsed_namespace, parsed_component_name, parsed_endpoint_name = parse_endpoint(
+        encode_endpoint
+    )
+
+    encode_worker_client = (
+        await runtime.namespace(parsed_namespace)
+        .component(parsed_component_name)
+        .endpoint(parsed_endpoint_name)
+        .client()
+    )
+
+    handler = MultimodalProcessorHandler(component, config, encode_worker_client)
+
+    logging.info("Waiting for Encoder Worker Instances ...")
+    await encode_worker_client.wait_for_instances()
+
+    async def register_model():
+        """Register the model and signal readiness"""
+        registration_success = await register_llm_with_runtime_config(
+            None,  # engine,
+            generate_endpoint,
+            server_args,
+            dynamo_args,
+            input_type=ModelInput.Text,
+        )
+
+        if not registration_success:
+            logging.error("Model registration failed; shutting down")
+            runtime.shutdown()
+            raise RuntimeError("Model registration failed")
+
+        logging.info("Model registration succeeded; processing queued requests")
+
+    try:
+        # Start endpoint immediately and register model concurrently
+        # Requests queue until ready_event is set
+        await asyncio.gather(
+            generate_endpoint.serve_endpoint(
+                handler.generate,
+                graceful_shutdown=True,
+                metrics_labels=[("model", server_args.served_model_name)],
+            ),
+            register_model(),
+        )
+    except Exception as e:
+        logging.error(f"Failed to serve endpoints: {e}")
+        raise
+    finally:
+        handler.cleanup()
+
+
+async def init_multimodal_encode_worker(runtime: DistributedRuntime, config: Config):
+    """Initialize multimodal encode worker component"""
+    server_args, dynamo_args = config.server_args, config.dynamo_args
+
+    component = runtime.namespace(dynamo_args.namespace).component(
+        dynamo_args.component
+    )
+    await component.create_service()
+
+    generate_endpoint = component.endpoint(dynamo_args.endpoint)
+
+    # For encode worker, we need to connect to the downstream worker (LLM worker)
+    # Default endpoint for LLM worker
+    llm_endpoint = f"dyn://{dynamo_args.namespace}.backend.generate"
+    parsed_namespace, parsed_component_name, parsed_endpoint_name = parse_endpoint(
+        llm_endpoint
+    )
+
+    pd_worker_client = (
+        await runtime.namespace(parsed_namespace)
+        .component(parsed_component_name)
+        .endpoint(parsed_endpoint_name)
+        .client()
+    )
+
+    handler = MultimodalEncodeWorkerHandler(component, config, pd_worker_client)
+    await handler.async_init(runtime)
+
+    await pd_worker_client.wait_for_instances()
+
+    tasks = [
+        generate_endpoint.serve_endpoint(
+            handler.generate,
+            graceful_shutdown=True,
+            metrics_labels=[("model", server_args.served_model_name)],
+        )
+    ]
+
+    try:
+        await asyncio.gather(*tasks)
+    except Exception as e:
+        logging.error(f"Failed to serve endpoints: {e}")
+        raise
+    finally:
+        handler.cleanup()
+
+
+async def init_multimodal_worker(runtime: DistributedRuntime, config: Config):
+    """Initialize multimodal worker component for aggregated or decode mode"""
+    server_args, dynamo_args = config.server_args, config.dynamo_args
+
+    component = runtime.namespace(dynamo_args.namespace).component(
+        dynamo_args.component
+    )
+    await component.create_service()
+
+    generate_endpoint = component.endpoint(dynamo_args.endpoint)
+
+    engine = sgl.Engine(server_args=server_args)
+
+    # Setup handler based on serving mode
+    if config.serving_mode == DisaggregationMode.DECODE:
+        # Decode mode: create prefill client
+        prefill_endpoint = f"dyn://{dynamo_args.namespace}.prefill.generate"
+        parsed_namespace, parsed_component_name, parsed_endpoint_name = parse_endpoint(
+            prefill_endpoint
+        )
+
+        logging.info("Initializing prefill client for multimodal decode worker")
+        prefill_client = (
+            await runtime.namespace(parsed_namespace)
+            .component(parsed_component_name)
+            .endpoint(parsed_endpoint_name)
+            .client()
+        )
+        handler = MultimodalWorkerHandler(
+            component, engine, config, None, None, prefill_client
+        )
+    else:
+        # Aggregated mode: no prefill client needed
+        handler = MultimodalWorkerHandler(component, engine, config)
+
+    # Initialize async components
+    await handler.async_init()
+
+    try:
+        await generate_endpoint.serve_endpoint(
+            handler.generate,
+            metrics_labels=[("model", server_args.served_model_name)],
+            graceful_shutdown=True,
+        )
+    except Exception as e:
+        logging.error(f"Failed to serve endpoints: {e}")
+        raise
+    finally:
+        handler.cleanup()
+
+
+async def init_multimodal_prefill_worker(runtime: DistributedRuntime, config: Config):
+    """Initialize multimodal prefill worker component"""
+    server_args, dynamo_args = config.server_args, config.dynamo_args
+
+    engine = sgl.Engine(server_args=server_args)
+
+    component = runtime.namespace(dynamo_args.namespace).component(
+        dynamo_args.component
+    )
+    await component.create_service()
+
+    generate_endpoint = component.endpoint(dynamo_args.endpoint)
+
+    handler = MultimodalPrefillWorkerHandler(component, engine, config)
+    await handler.async_init()
+
+    health_check_payload = SglangPrefillHealthCheckPayload(engine).to_dict()
+
+    try:
+        await asyncio.gather(
+            generate_endpoint.serve_endpoint(
+                handler.generate,
+                graceful_shutdown=True,
+                metrics_labels=[("model", server_args.served_model_name)],
+                health_check_payload=health_check_payload,
+            )
+        )
+    except Exception as e:
+        logging.error(f"Failed to serve endpoints: {e}")
+        raise
+    finally:
+        handler.cleanup()
+
+
 async def graceful_shutdown(runtime):
     logging.info("Received shutdown signal, shutting down DistributedRuntime")
     runtime.shutdown()

components/src/dynamo/sglang/register.py

@@ -17,6 +17,7 @@ async def register_llm_with_runtime_config(
     endpoint: Endpoint,
     server_args: ServerArgs,
     dynamo_args: DynamoArgs,
+    input_type: Optional[ModelInput] = ModelInput.Tokens,
 ) -> bool:
     """Register LLM with runtime config
 
@@ -24,7 +25,7 @@ async def register_llm_with_runtime_config(
         bool: True if registration succeeded, False if it failed
     """
     runtime_config = await _get_runtime_config(engine, server_args, dynamo_args)
-    input_type = ModelInput.Tokens
+    input_type = input_type
     output_type = ModelType.Chat | ModelType.Completions
     if not server_args.skip_tokenizer_init:
         logging.warning(

components/src/dynamo/sglang/request_handlers/__init__.py

@@ -5,10 +5,22 @@ from .decode_handler import DecodeWorkerHandler
 
 # Base handlers
 from .handler_base import BaseWorkerHandler
+
+# Multimodal handlers
+from .multimodal_encode_worker_handler import MultimodalEncodeWorkerHandler
+from .multimodal_processor_handler import MultimodalProcessorHandler
+from .multimodal_worker_handler import (
+    MultimodalPrefillWorkerHandler,
+    MultimodalWorkerHandler,
+)
 from .prefill_handler import PrefillWorkerHandler
 
 __all__ = [
     "BaseWorkerHandler",
     "DecodeWorkerHandler",
     "PrefillWorkerHandler",
+    "MultimodalProcessorHandler",
+    "MultimodalEncodeWorkerHandler",
+    "MultimodalWorkerHandler",
+    "MultimodalPrefillWorkerHandler",
 ]

components/src/dynamo/sglang/request_handlers/multimodal_encode_worker_handler.py

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import logging
+from typing import AsyncIterator
+
+import torch
+from sglang.srt.conversation import chat_templates
+from transformers import AutoImageProcessor, AutoModel, AutoTokenizer
+
+import dynamo.nixl_connect as connect
+from dynamo._core import Client, Component
+from dynamo.runtime import DistributedRuntime
+from dynamo.sglang.args import Config
+from dynamo.sglang.request_handlers.handler_base import BaseWorkerHandler
+from dynamo.sglang.utils.multimodal_encode_utils import encode_image_embeddings
+from dynamo.sglang.utils.multimodal_image_loader import ImageLoader
+from dynamo.sglang.utils.multimodal_protocol import SglangMultimodalRequest
+
+logger = logging.getLogger(__name__)
+
+try:
+    import cupy as array_module
+
+    if not array_module.cuda.is_available():
+        raise ImportError("CUDA is not available.")
+    DEVICE = "cuda"
+    logger.info("Using cupy for array operations (GPU mode).")
+except ImportError as e:
+    logger.warning(f"Failed to import cupy, falling back to numpy: {e}.")
+    import numpy as array_module
+
+    DEVICE = "cpu"
+
+CACHE_SIZE_MAXIMUM = 8
+
+
+class MultimodalEncodeWorkerHandler(BaseWorkerHandler):
+    """
+    Handler for multimodal encode worker component that processes images/videos
+    and forwards them to the downstream worker.
+    """
+
+    def __init__(
+        self,
+        component: Component,
+        config: Config,
+        pd_worker_client: Client,
+    ) -> None:
+        super().__init__(component, engine=None, config=config)
+        self.pd_worker_client = pd_worker_client
+        self.model = config.server_args.model_path
+
+        self.image_loader = ImageLoader(cache_size=CACHE_SIZE_MAXIMUM)
+
+        self.image_processor = AutoImageProcessor.from_pretrained(
+            self.model, trust_remote_code=True
+        )
+        self.vision_model = AutoModel.from_pretrained(
+            self.model,
+            device_map="auto",
+            torch_dtype=torch.float16,
+            trust_remote_code=True,
+        )
+
+        # Load tokenizer to convert image token string to integer ID
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.model, trust_remote_code=True
+        )
+
+        # Get image token string and handle it properly
+        image_token_str = (
+            chat_templates[getattr(config.server_args, "chat_template")]
+            .copy()
+            .image_token
+        )
+
+        # For Qwen2.5-VL, the image token might be multiple tokens
+        if image_token_str == "<|vision_start|><|image_pad|><|vision_end|>":
+            # These are likely the individual special tokens for Qwen2.5-VL
+            image_pad_id = self.tokenizer.convert_tokens_to_ids("<|image_pad|>")
+
+            # Use the image_pad token as the main image token
+            self.image_token_id = image_pad_id
+        else:
+            # Fallback for other models
+            self.image_token_id = self.tokenizer.convert_tokens_to_ids(image_token_str)
+
+        self.min_workers = 1
+
+    def cleanup(self):
+        pass
+
+    async def generate(self, request: SglangMultimodalRequest) -> AsyncIterator[str]:
+        if not isinstance(request, SglangMultimodalRequest):
+            if isinstance(request, str):
+                request = SglangMultimodalRequest.model_validate_json(request)
+            else:
+                request = SglangMultimodalRequest.model_validate(request)
+
+        # The following steps encode the requested image for SGLang:
+        # 1. Open the image from the provided URL.
+        # 2. Process the image using the processor (which handles tokenization).
+        # 3. Extract input_ids and image data from processed result.
+        # 4. Run the image through the vision model to get precomputed embeddings.
+        # 5. Create SGLang-specific multimodal data format.
+        # 6. Create a descriptor for the embeddings and send to downstream worker.
+
+        try:
+            if not request.multimodal_input.image_url:
+                raise ValueError("image_url is required for the encode worker.")
+
+            image = await self.image_loader.load_image(
+                request.multimodal_input.image_url
+            )
+
+            image_embeds = self.image_processor(images=image, return_tensors="pt")
+            precomputed_embeddings = encode_image_embeddings(
+                model_name=self.model,
+                image_embeds=image_embeds,
+                vision_encoder=self.vision_model,
+                projector=None,
+            )
+
+            image_grid_thw = (
+                image_embeds["image_grid_thw"].tolist()
+                if "image_grid_thw" in image_embeds
+                else None
+            )
+
+            # Store the image data info in the request for downstream
+            request.image_grid_thw = image_grid_thw
+            request.embeddings_shape = tuple(precomputed_embeddings.shape)
+
+            # Replace the single image token with multiple image tokens based on embedding shape
+            image_token_id_index = request.request.token_ids.index(self.image_token_id)
+
+            num_image_tokens = precomputed_embeddings.shape[
+                1
+            ]  # Number of image patches
+            # Replace single image token with multiple image tokens
+            request.request.token_ids = (
+                request.request.token_ids[:image_token_id_index]
+                + [self.image_token_id] * num_image_tokens
+                + request.request.token_ids[
+                    image_token_id_index + 1 :
+                ]  # Skip the original token
+            )
+
+            # Create descriptor for the multimodal data
+            descriptor = connect.Descriptor(precomputed_embeddings)
+
+            with self._connector.create_readable(descriptor) as readable:
+                request.serialized_request = readable.metadata()
+
+                logger.debug(f"Request: {request.model_dump_json()}")
+
+                # Get the response generator from downstream worker
+                response_generator = await self.pd_worker_client.round_robin(
+                    request.model_dump_json()
+                )
+                await readable.wait_for_completion()
+
+                async for response in response_generator:
+                    yield response.data() if hasattr(response, "data") else str(
+                        response
+                    )
+
+        except Exception as e:
+            logger.error(f"Error processing request: {e}")
+            raise
+
+    async def async_init(self, runtime: DistributedRuntime):
+        logger.info("Startup started.")
+        # Create and initialize a dynamo connector for this worker.
+        # We'll needs this to move data between this worker and remote workers efficiently.
+        self._connector = connect.Connector()
+        await self._connector.initialize()
+
+        logger.info("Startup completed.")

components/src/dynamo/sglang/request_handlers/multimodal_processor_handler.py

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import json
+import logging
+import time
+import uuid
+from typing import Any, Dict
+
+from transformers import AutoTokenizer
+
+from dynamo._core import Client, Component
+from dynamo.sglang.args import Config
+from dynamo.sglang.request_handlers.handler_base import BaseWorkerHandler
+from dynamo.sglang.utils.multimodal_chat_processor import (
+    multimodal_request_to_sglang,
+    process_sglang_stream_response,
+)
+from dynamo.sglang.utils.multimodal_protocol import (
+    MultiModalInput,
+    MultiModalRequest,
+    SglangMultimodalRequest,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class MultimodalProcessorHandler(BaseWorkerHandler):
+    """
+    Handler for multimodal processor component that processes multimodal requests
+    and forwards them to the encode worker.
+    """
+
+    def __init__(
+        self,
+        component: Component,
+        config: Config,
+        encode_worker_client: Client,
+    ):
+        super().__init__(component, engine=None, config=config)
+        self.encode_worker_client = encode_worker_client
+        self.chat_template = getattr(config.server_args, "chat_template", "qwen2-vl")
+        self.model = config.server_args.model_path
+
+        # Initialize tokenizer for the model
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.model,
+            trust_remote_code=True,
+            use_fast=True,
+            padding_side="left",
+            truncation_side="left",
+        )
+
+    def cleanup(self):
+        pass
+
+    async def generate(self, raw_request: MultiModalRequest):
+        if not isinstance(raw_request, MultiModalRequest):
+            # If the request is not MultiModalRequest, convert it to MultiModalRequest
+            raw_request = MultiModalRequest.model_validate(raw_request)
+
+        multimodal_input = MultiModalInput()
+
+        for message in raw_request.messages:
+            for item in message.content:
+                if item.type == "image_url":
+                    multimodal_input.image_url = item.image_url.url
+                elif item.type == "video_url":
+                    if multimodal_input.image_url is not None:
+                        raise ValueError("Cannot provide both image and video URLs")
+                    multimodal_input.video_url = item.video_url.url
+
+        if multimodal_input.image_url is None and multimodal_input.video_url is None:
+            raise ValueError("Either image URL or video URL is required")
+
+        async for response in self._generate(raw_request, multimodal_input):
+            logger.debug(
+                f"Generated response type {type(response)}, content: {response}"
+            )
+            yield response
+
+    async def _generate(
+        self,
+        raw_request: MultiModalRequest,
+        multimodal_input: MultiModalInput,
+    ):
+        # Generate a unique request ID for tracking
+        request_id = str(uuid.uuid4().hex)
+        logger.debug(f"Got raw request: {raw_request}")
+
+        # Create SGLang conversation prompt
+        sglang_request = multimodal_request_to_sglang(
+            raw_request, self.tokenizer, self.chat_template
+        )
+
+        worker_request = SglangMultimodalRequest(
+            request=sglang_request,
+            multimodal_input=multimodal_input,
+        )
+
+        # Send to encoder worker
+        response_generator = await self.encode_worker_client.round_robin(
+            worker_request.model_dump_json()
+        )
+
+        # Process and yield SGLang responses
+        finished_sent = False
+        accumulated_text = ""
+
+        async for resp in response_generator:
+            try:
+                # Handle Annotated response objects from Dynamo (like vLLM pattern but for SGLang)
+                if hasattr(resp, "data"):
+                    # Extract data from Dynamo Annotated response
+                    raw_data = resp.data
+                    if callable(raw_data):
+                        raw_data = raw_data()
+
+                    if isinstance(raw_data, str):
+                        try:
+                            response_data = json.loads(raw_data)
+                        except json.JSONDecodeError:
+                            response_data = {"text": raw_data, "finished": False}
+                    else:
+                        response_data = raw_data
+                elif isinstance(resp, str):
+                    try:
+                        response_data = json.loads(resp)
+                    except json.JSONDecodeError:
+                        response_data = {"text": resp, "finished": False}
+                else:
+                    response_data = resp
+
+                # Use SGLang chat_processor for detokenization
+                (
+                    text_content,
+                    accumulated_text,
+                    is_finished,
+                ) = process_sglang_stream_response(
+                    response_data, self.tokenizer, accumulated_text
+                )
+
+                # Create OpenAI-compatible response (following vLLM-like pattern but for SGLang)
+                if text_content or is_finished:
+                    choice: Dict[str, Any] = {
+                        "index": 0,
+                        "delta": {},
+                        "finish_reason": None,
+                    }
+                    delta: Dict[str, str] = choice["delta"]  # Type-safe access
+
+                    # Add role for first message or when there's content
+                    if text_content and not finished_sent:
+                        delta["role"] = "assistant"
+
+                    # Add content if available
+                    if text_content:
+                        delta["content"] = text_content
+
+                    # Set finish reason if completed
+                    if is_finished:
+                        choice["finish_reason"] = response_data.get(
+                            "finish_reason", "stop"
+                        )
+                        if not finished_sent and not text_content:
+                            # Final chunk needs role if it's the first chunk
+                            delta["role"] = "assistant"
+
+                    response_json = {
+                        "id": f"chatcmpl-{request_id}",
+                        "object": "chat.completion.chunk",
+                        "created": int(time.time()),
+                        "model": self.model,
+                        "choices": [choice],
+                    }
+
+                    # Add usage only for final response
+                    if is_finished:
+                        response_json["usage"] = {
+                            "prompt_tokens": 0,
+                            "completion_tokens": len(accumulated_text.split())
+                            if accumulated_text
+                            else 0,
+                            "total_tokens": len(accumulated_text.split())
+                            if accumulated_text
+                            else 0,
+                        }
+
+                    yield response_json
+
+                    if is_finished:
+                        finished_sent = True
+                        break
+
+            except Exception as e:
+                logger.error(f"Error processing SGLang response: {e}")
+                error_response = {
+                    "id": f"chatcmpl-{request_id}",
+                    "object": "chat.completion.chunk",
+                    "created": int(time.time()),
+                    "model": self.model,
+                    "choices": [
+                        {
+                            "index": 0,
+                            "delta": {
+                                "role": "assistant",
+                                "content": f"Error: {str(e)}",
+                            },
+                            "finish_reason": "stop",
+                        }
+                    ],
+                }
+                yield error_response
+                break

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

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import logging
+
+from sglang.srt.conversation import chat_templates
+
+logger = logging.getLogger(__name__)
+
+
+def clean_addcriterion(text: str) -> str:
+    """
+    Removes the addCriterion prefix from the text output.
+
+    To prevent addCriterion from appearing in outputs, an assistant placeholder must be added to the conversation.
+    However, adding the assistant placeholder causes subsequent requests to fail with shape mismatch errors on the engine side.
+    The root cause is still under investigation, so this temporary workaround is in place to maintain functionality.
+    """
+
+    if text.startswith(" addCriterion"):
+        cleaned_text = text[13:].lstrip()  # 12 = len(" addCriterion")
+        logger.debug(
+            f"🛠️ HACK: Removed ' addCriterion' prefix: '{text[:20]}...' -> '{cleaned_text[:20]}...'"
+        )
+        return cleaned_text
+
+    if text.startswith("addCriterion"):
+        cleaned_text = text[12:].lstrip()  # 11 = len("addCriterion")
+        logger.debug(
+            f"🛠️ HACK: Removed 'addCriterion' prefix: '{text[:20]}...' -> '{cleaned_text[:20]}...'"
+        )
+        return cleaned_text
+
+    return text
+
+
+def multimodal_request_to_sglang(raw_request, tokenizer, chat_template):
+    conv = chat_templates[chat_template].copy()
+    conv.messages = []
+
+    # Convert messages into SGLang conversation
+    for msg in raw_request.messages:
+        if msg.role == "system":
+            conv.system_message = msg.content
+        elif msg.role == "user":
+            text_parts = []
+            for part in msg.content:
+                if part.type == "text":
+                    text_parts.append(part.text)
+                elif part.type == "image_url":
+                    text_parts.append(conv.image_token)
+            conv.append_message(conv.roles[0], " ".join(text_parts))
+        elif msg.role == "assistant":
+            conv.append_message(conv.roles[1], msg.content)
+
+    logger.debug(f"conv: {conv}")
+
+    # Tokenize and prepare input_ids
+    processed = tokenizer(text=conv.get_prompt(), return_tensors="pt")
+    input_ids = processed["input_ids"][0].tolist()
+
+    # Build the SGLang request dict
+    sglang_request = {
+        "model": raw_request.model,
+        "token_ids": input_ids,
+        "batch_token_ids": None,
+        "stop_conditions": {"max_tokens": raw_request.max_tokens or None},
+        "sampling_options": {"temperature": raw_request.temperature or 0.7},
+        "eos_token_ids": [tokenizer.eos_token_id],
+        "annotations": [],
+        "stream": raw_request.stream if raw_request.stream is not None else False,
+    }
+
+    return sglang_request
+
+
+def detokenize_sglang_response(response_data, tokenizer):
+    """
+    Detokenize SGLang response token IDs to text.
+
+    Args:
+        response_data: Dictionary containing token_ids and other response data
+        tokenizer: The tokenizer to use for detokenization
+
+    Returns:
+        String containing the detokenized text, empty string if no tokens
+    """
+    try:
+        # Handle Annotated objects from Dynamo (following vLLM-like pattern)
+        if hasattr(response_data, "data"):
+            try:
+                import json
+
+                raw_data = response_data.data
+                # Handle callable data method
+                if callable(raw_data):
+                    raw_data = raw_data()
+                response_data = (
+                    json.loads(raw_data) if isinstance(raw_data, str) else raw_data
+                )
+            except (json.JSONDecodeError, AttributeError):
+                try:
+                    raw_data = response_data.data
+                    if callable(raw_data):
+                        raw_data = raw_data()
+                    response_data = {"text": str(raw_data), "finished": False}
+                except Exception:
+                    response_data = {"text": str(response_data), "finished": False}
+
+        # Ensure response_data is a dictionary
+        if not isinstance(response_data, dict):
+            return clean_addcriterion(str(response_data))
+
+        # Get text content - detokenize if needed
+        if "text" in response_data and response_data["text"]:
+            return clean_addcriterion(response_data["text"])
+        elif "token_ids" in response_data and response_data["token_ids"]:
+            token_ids = response_data["token_ids"]
+            if isinstance(token_ids, list) and token_ids:
+                # Detokenize token IDs to get text
+                text_content = tokenizer.decode(token_ids, skip_special_tokens=True)
+                logger.debug(
+                    f"Detokenized {len(token_ids)} tokens to: '{text_content}'"
+                )
+                return clean_addcriterion(text_content)
+
+        # Return empty string if no content to detokenize
+        return ""
+
+    except Exception as e:
+        logger.error(f"Failed to detokenize response: {e}")
+        return f"[Detokenization error: {e}]"
+
+
+def process_sglang_stream_response(response_data, tokenizer, accumulated_text=""):
+    """
+    Process a single SGLang streaming response with efficient detokenization.
+
+    Args:
+        response_data: Dictionary containing SGLang response data
+        tokenizer: The tokenizer to use for detokenization
+        accumulated_text: Previously accumulated text for context
+
+    Returns:
+        Tuple of (text_content, updated_accumulated_text, is_finished)
+    """
+    try:
+        # Handle Annotated objects from Dynamo (following vLLM-like pattern)
+        if hasattr(response_data, "data"):
+            try:
+                import json
+
+                raw_data = response_data.data
+                # Handle callable data method
+                if callable(raw_data):
+                    raw_data = raw_data()
+                response_data = (
+                    json.loads(raw_data) if isinstance(raw_data, str) else raw_data
+                )
+            except (json.JSONDecodeError, AttributeError):
+                try:
+                    raw_data = response_data.data
+                    if callable(raw_data):
+                        raw_data = raw_data()
+                    response_data = {"text": str(raw_data), "finished": False}
+                except Exception:
+                    response_data = {"text": str(response_data), "finished": False}
+
+        # Ensure response_data is a dictionary
+        if not isinstance(response_data, dict):
+            response_data = {"text": str(response_data), "finished": False}
+
+        # Detokenize the current response
+        text_content = detokenize_sglang_response(response_data, tokenizer)
+
+        # Update accumulated text
+        new_accumulated = accumulated_text + text_content
+
+        # Check if this is the final response
+        is_finished = response_data.get("finished", False) or response_data.get(
+            "finish_reason"
+        )
+
+        return text_content, new_accumulated, is_finished
+
+    except Exception as e:
+        logger.error(f"Error processing SGLang stream response: {e}")
+        return f"[Processing error: {e}]", accumulated_text, True

components/src/dynamo/sglang/utils/multimodal_encode_utils.py

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import logging
+from typing import Any, Dict, Optional
+
+import torch
+
+logger = logging.getLogger(__name__)
+
+
+class SupportedModels:
+    """Supported multimodal model identifiers"""
+
+    QWEN_2_5_VL_7B = "Qwen/Qwen2.5-VL-7B-Instruct"
+
+
+def get_qwen_image_features(
+    vision_encoder: torch.nn.Module, image_embeds: Dict[str, Any]
+) -> torch.Tensor:
+    """
+    Extract image features using Qwen-style vision encoder.
+
+    Args:
+        vision_encoder: The vision encoder model
+        image_embeds: Dictionary containing pixel values and grid information
+
+    Returns:
+        Processed image features tensor
+
+    Raises:
+        ValueError: If grid_thw is not provided for Qwen model
+    """
+    pixel_values = image_embeds["pixel_values"].to(vision_encoder.device)
+
+    grid_thw = image_embeds.get("image_grid_thw", None)
+    if grid_thw is not None:
+        grid_thw = grid_thw.to(vision_encoder.device)
+        logger.debug(f"Qwen grid_thw shape: {grid_thw.shape}")
+    else:
+        raise ValueError("grid_thw is not provided")
+
+    return (
+        vision_encoder.get_image_features(pixel_values, grid_thw)  # type: ignore
+        if grid_thw is not None
+        else vision_encoder.get_image_features(pixel_values)  # type: ignore
+    )
+
+
+def encode_image_embeddings(
+    model_name: str,
+    image_embeds: Dict[str, Any],
+    vision_encoder: torch.nn.Module,
+    projector: Optional[torch.nn.Module] = None,
+) -> torch.Tensor:
+    """
+    Encode image embeddings using the appropriate model-specific encoder.
+
+    Args:
+        model_name: The model identifier
+        image_embeds: Dictionary containing processed image data
+        vision_encoder: The vision encoder module
+        projector: The multimodal projector (required for LLaVA-style models)
+
+    Returns:
+        Encoded embeddings tensor with normalized shape
+
+    Raises:
+        ValueError: If projector is missing for LLaVA models
+        NotImplementedError: If model is not supported
+    """
+    with torch.no_grad():
+        # Route through the correct encoder based on model
+        if model_name == SupportedModels.QWEN_2_5_VL_7B:
+            embeddings = get_qwen_image_features(vision_encoder, image_embeds)
+
+        else:
+            raise NotImplementedError(f"Model not supported: {model_name}")
+
+        # Normalize output shape
+        if isinstance(embeddings, (tuple, list)):
+            embeddings = embeddings[0]
+        embeddings = embeddings.unsqueeze(0) if embeddings.ndim == 2 else embeddings
+
+        return embeddings

components/src/dynamo/sglang/utils/multimodal_image_loader.py

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import asyncio
+import base64
+import binascii
+import logging
+from io import BytesIO
+from typing import Optional
+from urllib.parse import urlparse
+
+import httpx
+from PIL import Image
+
+logger = logging.getLogger(__name__)
+
+# Global HTTP client instance
+_global_http_client: Optional[httpx.AsyncClient] = None
+
+
+def get_http_client(timeout: float = 60.0) -> httpx.AsyncClient:
+    """
+    Get or create a shared HTTP client instance.
+
+    Args:
+        timeout: Timeout for HTTP requests
+
+    Returns:
+        Shared HTTP client instance
+    """
+    global _global_http_client
+
+    if _global_http_client is None or _global_http_client.is_closed:
+        _global_http_client = httpx.AsyncClient(
+            timeout=timeout,
+            follow_redirects=True,
+            limits=httpx.Limits(max_keepalive_connections=20, max_connections=100),
+        )
+        logger.info(f"Shared HTTP client initialized with timeout={timeout}s")
+
+    return _global_http_client
+
+
+class ImageLoader:
+    CACHE_SIZE_MAXIMUM = 8
+
+    def __init__(
+        self, cache_size: int = CACHE_SIZE_MAXIMUM, http_timeout: float = 30.0
+    ):
+        self._http_timeout = http_timeout
+        self._image_cache: dict[str, Image.Image] = {}
+        self._cache_queue: asyncio.Queue[str] = asyncio.Queue(maxsize=cache_size)
+
+    async def load_image(self, image_url: str) -> Image.Image:
+        parsed_url = urlparse(image_url)
+
+        # For HTTP(S) URLs, check cache first
+        if parsed_url.scheme in ("http", "https"):
+            image_url_lower = image_url.lower()
+            if image_url_lower in self._image_cache:
+                logger.debug(f"Image found in cache for URL: {image_url}")
+                return self._image_cache[image_url_lower]
+
+        try:
+            if parsed_url.scheme == "data":
+                # Parse data URL format: data:[<media type>][;base64],<data>
+                if not parsed_url.path.startswith("image/"):
+                    raise ValueError("Data URL must be an image type")
+
+                # Split the path into media type and data
+                media_type, data = parsed_url.path.split(",", 1)
+                if ";base64" not in media_type:
+                    raise ValueError("Data URL must be base64 encoded")
+
+                try:
+                    image_bytes = base64.b64decode(data)
+                    image_data = BytesIO(image_bytes)
+                except binascii.Error as e:
+                    raise ValueError(f"Invalid base64 encoding: {e}")
+            elif parsed_url.scheme in ("http", "https"):
+                http_client = get_http_client(self._http_timeout)
+
+                response = await http_client.get(image_url)
+                response.raise_for_status()
+
+                if not response.content:
+                    raise ValueError("Empty response content from image URL")
+
+                image_data = BytesIO(response.content)
+            else:
+                raise ValueError(f"Invalid image source scheme: {parsed_url.scheme}")
+
+            # PIL is sync, so offload to a thread to avoid blocking the event loop
+            image = await asyncio.to_thread(Image.open, image_data)
+
+            # Validate image format and convert to RGB
+            if image.format not in ("JPEG", "PNG", "WEBP"):
+                raise ValueError(f"Unsupported image format: {image.format}")
+
+            image_converted = image.convert("RGB")
+
+            # Cache HTTP(S) URLs
+            if parsed_url.scheme in ("http", "https"):
+                image_url_lower = image_url.lower()
+                # Cache the image for future use, and evict the oldest image if the cache is full
+                if self._cache_queue.full():
+                    oldest_image_url = await self._cache_queue.get()
+                    del self._image_cache[oldest_image_url]
+
+                self._image_cache[image_url_lower] = image_converted
+                await self._cache_queue.put(image_url_lower)
+
+            return image_converted
+
+        except httpx.HTTPError as e:
+            logger.error(f"HTTP error loading image: {e}")
+            raise
+        except Exception as e:
+            logger.error(f"Error loading image: {e}")
+            raise ValueError(f"Failed to load image: {e}")

components/src/dynamo/sglang/utils/multimodal_protocol.py

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Any, List, Literal, Optional, Tuple, Union
+
+from pydantic import BaseModel, ConfigDict, Field
+
+import dynamo.nixl_connect as connect
+from dynamo.sglang.protocol import PreprocessedRequest
+
+TokenIdType = int
+
+
+class TextContent(BaseModel):
+    type: Literal["text"]
+    text: str
+
+
+class ImageURLDetail(BaseModel):
+    url: str
+
+
+class ImageContent(BaseModel):
+    type: Literal["image_url"]
+    image_url: ImageURLDetail
+
+
+class VideoURLDetail(BaseModel):
+    url: str
+
+
+class VideoContent(BaseModel):
+    type: Literal["video_url"]
+    video_url: VideoURLDetail
+
+
+MessageContent = Union[TextContent, ImageContent, VideoContent]
+
+
+class ChatMessage(BaseModel):
+    role: Literal["user", "system", "assistant"]
+    content: List[MessageContent]
+
+
+class MultiModalRequest(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+    model: str
+    messages: List[ChatMessage]
+    max_tokens: Optional[int] = None
+    temperature: Optional[float] = None
+    stream: Optional[bool] = False
+
+
+class MultiModalInput(BaseModel):
+    image_url: Optional[str] = None
+    video_url: Optional[str] = None
+
+
+class SglangMultimodalRequest(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+    request: PreprocessedRequest
+    multimodal_input: Optional[MultiModalInput] = Field(default_factory=MultiModalInput)
+    image_grid_thw: Optional[List[Any]] = None
+    embeddings_shape: Optional[
+        Union[Tuple[int, int, int], Tuple[int, int, int, int]]
+    ] = None
+    serialized_request: Optional[connect.RdmaMetadata] = None
+
+
+class DisaggSglangMultimodalRequest(BaseModel):
+    request: SglangMultimodalRequest
+    sampling_params: dict
+    data_parallel_rank: Optional[int] = None

examples/multimodal/README.md

@@ -39,16 +39,16 @@ git checkout $(git describe --tags $(git rev-list --tags --max-count=1))
 - processor: Tokenizes the prompt and passes it to the VllmEncodeWorker.
 - frontend: HTTP endpoint to handle incoming requests.
 
-### Graph
+### Workflow
 
-In this graph, we have two workers, [VllmEncodeWorker](components/encode_worker.py) and [VllmPDWorker](components/worker.py).
+In this workflow, we have two workers, [VllmEncodeWorker](components/encode_worker.py) and [VllmPDWorker](components/worker.py).
 The VllmEncodeWorker is responsible for encoding the image and passing the embeddings to the VllmPDWorker via a combination of NATS and RDMA.
 The work complete event is sent via NATS, while the embeddings tensor is transferred via RDMA through the NIXL interface.
-Its VllmPDWorker then prefills and decodes the prompt, just like the [LLM aggregated serving](/components/backends/vllm/README.md) example.
+Its VllmPDWorker then prefills and decodes the prompt, just like the [LLM aggregated serving](../../components/backends/vllm/README.md) example.
 By separating the encode from the prefill and decode stages, we can have a more flexible deployment and scale the
 VllmEncodeWorker independently from the prefill and decode workers if needed.
 
-This figure shows the flow of the graph:
+This figure illustrates the workflow:
 ```mermaid
 flowchart LR
   HTTP --> processor
@@ -115,16 +115,16 @@ You should see a response similar to this:
 - processor: Tokenizes the prompt and passes it to the VllmEncodeWorker.
 - frontend: HTTP endpoint to handle incoming requests.
 
-### Graph
+### Workflow
 
-In this graph, we have three workers, [VllmEncodeWorker](components/encode_worker.py), [VllmDecodeWorker](components/worker.py), and [VllmPDWorker](components/worker.py).
+In this workflow, we have three workers, [VllmEncodeWorker](components/encode_worker.py), [VllmDecodeWorker](components/worker.py), and [VllmPDWorker](components/worker.py).
 For the Llava model, embeddings are only required during the prefill stage. As such, the VllmEncodeWorker is connected directly to the prefill worker.
 The VllmEncodeWorker is responsible for encoding the image and passing the embeddings to the prefill worker via a combination of NATS and RDMA.
 Its work complete event is sent via NATS, while the embeddings tensor is transferred via RDMA through the NIXL interface.
 The prefill worker performs the prefilling step and forwards the KV cache to the decode worker for decoding.
-For more details on the roles of the prefill and decode workers, refer to the [LLM disaggregated serving](/components/backends/vllm/README.md) example.
+For more details on the roles of the prefill and decode workers, refer to the [LLM disaggregated serving](../../components/backends/vllm/README.md) example.
 
-This figure shows the flow of the graph:
+This figure illustrates the workflow:
 ```mermaid
 flowchart LR
   HTTP --> processor
@@ -201,11 +201,11 @@ of the model per node.
 - processor: Tokenizes the prompt and passes it to the VllmPDWorker.
 - frontend: HTTP endpoint to handle incoming requests.
 
-#### Graph
+#### Workflow
 
-In this graph, we have [VllmPDWorker](components/worker.py) which will encode the image, prefill and decode the prompt, just like the [LLM aggregated serving](/components/backends/vllm/README.md) example.
+In this workflow, we have [VllmPDWorker](components/worker.py) which will encode the image, prefill and decode the prompt, just like the [LLM aggregated serving](/components/backends/vllm/README.md) example.
 
-This figure shows the flow of the graph:
+This figure illustrates the workflow:
 ```mermaid
 flowchart LR
   HTTP --> processor
@@ -263,13 +263,13 @@ You should see a response similar to this:
 - processor: Tokenizes the prompt and passes it to the VllmPDWorker.
 - frontend: HTTP endpoint to handle incoming requests.
 
-#### Graph
+#### Workflow
 
-In this graph, we have two workers, [VllmDecodeWorker](components/worker.py), and [VllmPDWorker](components/worker.py).
+In this workflow, we have two workers, [VllmDecodeWorker](components/worker.py), and [VllmPDWorker](components/worker.py).
 The prefill worker performs the encoding and prefilling steps and forwards the KV cache to the decode worker for decoding.
 For more details on the roles of the prefill and decode workers, refer to the [LLM disaggregated serving](/components/backends/vllm/README.md) example.
 
-This figure shows the flow of the graph:
+This figure illustrates the workflow:
 ```mermaid
 flowchart LR
   HTTP --> processor
@@ -337,16 +337,16 @@ This example demonstrates deploying an aggregated multimodal model that can proc
 - processor: Tokenizes the prompt and passes it to the VideoEncodeWorker.
 - frontend: HTTP endpoint to handle incoming requests.
 
-### Graph
+### Workflow
 
-In this graph, we have two workers, [VideoEncodeWorker](components/video_encode_worker.py) and [VllmPDWorker](components/worker.py).
+In this workflow, we have two workers, [VideoEncodeWorker](components/video_encode_worker.py) and [VllmPDWorker](components/worker.py).
 The VideoEncodeWorker is responsible for decoding the video into a series of frames. Unlike the image pipeline which generates embeddings,
 this pipeline passes the raw frames directly to the VllmPDWorker via a combination of NATS and RDMA.
 Its VllmPDWorker then prefills and decodes the prompt, just like the [LLM aggregated serving](/components/backends/vllm/README.md) example.
 By separating the video processing from the prefill and decode stages, we can have a more flexible deployment and scale the
 VideoEncodeWorker independently from the prefill and decode workers if needed.
 
-This figure shows the flow of the graph:
+This figure illustrates the workflow:
 ```mermaid
 flowchart LR
   HTTP --> processor
@@ -425,15 +425,15 @@ This example demonstrates deploying a disaggregated multimodal model that can pr
 - processor: Tokenizes the prompt and passes it to the VideoEncodeWorker.
 - frontend: HTTP endpoint to handle incoming requests.
 
-### Graph
+### Workflow
 
-In this graph, we have three workers, [VideoEncodeWorker](components/video_encode_worker.py), [VllmDecodeWorker](components/worker.py), and [VllmPDWorker](components/worker.py).
+In this workflow, we have three workers, [VideoEncodeWorker](components/video_encode_worker.py), [VllmDecodeWorker](components/worker.py), and [VllmPDWorker](components/worker.py).
 For the LLaVA-NeXT-Video-7B model, frames are only required during the prefill stage. As such, the VideoEncodeWorker is connected directly to the prefill worker.
 The VideoEncodeWorker is responsible for decoding the video into a series of frames and passing them to the prefill worker via RDMA.
 The prefill worker performs the prefilling step and forwards the KV cache to the decode worker for decoding.
 For more details on the roles of the prefill and decode workers, refer to the [LLM disaggregated serving](/components/backends/vllm/README.md) example.
 
-This figure shows the flow of the graph:
+This figure illustrates the workflow:
 ```mermaid
 flowchart LR
   HTTP --> processor

tests/serve/test_sglang.py

@@ -13,7 +13,11 @@ from tests.serve.common import (
     run_serve_deployment,
 )
 from tests.utils.engine_process import EngineConfig
-from tests.utils.payload_builder import chat_payload_default, completion_payload_default
+from tests.utils.payload_builder import (
+    chat_payload,
+    chat_payload_default,
+    completion_payload_default,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -86,6 +90,32 @@ sglang_configs = {
             )
         ],
     ),
+    "multimodal_agg_qwen": SGLangConfig(
+        name="multimodal_agg_qwen",
+        directory=sglang_dir,
+        script_name="multimodal_agg.sh",
+        marks=[pytest.mark.gpu_2],
+        model="Qwen/Qwen2.5-VL-7B-Instruct",
+        delayed_start=0,
+        timeout=360,
+        models_port=8000,
+        request_payloads=[
+            chat_payload(
+                [
+                    {"type": "text", "text": "What is in this image?"},
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": "http://images.cocodataset.org/test2017/000000155781.jpg"
+                        },
+                    },
+                ],
+                repeat_count=1,
+                expected_response=["bus"],
+                temperature=0.0,
+            )
+        ],
+    ),
 }