fix: Update vllm multimodal PD Disagg launch script (#5951)

examples/backends/vllm/launch/disagg_multimodal_llama.sh

@@ -5,6 +5,8 @@ set -ex
 
 # Default values
 HEAD_NODE=0
+MODEL_NAME="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
+EXTRA_ARGS=()
 
 # Parse command line arguments
 while [[ $# -gt 0 ]]; do
@@ -13,6 +15,10 @@ while [[ $# -gt 0 ]]; do
             HEAD_NODE=1
             shift 1
             ;;
+        --model)
+            MODEL_NAME=$2
+            shift 2
+            ;;
         -h|--help)
             echo "Usage: $0 [OPTIONS]"
             echo ""
@@ -20,6 +26,7 @@ while [[ $# -gt 0 ]]; do
             echo ""
             echo "Options:"
             echo "  --head-node          Run as head node. Head node will run the HTTP server, processor and prefill worker."
+            echo "  --model <model_name> Specify the VLM model to use (default: $MODEL_NAME)"
             echo "  -h, --help           Show this help message"
             echo ""
             echo "Examples:"
@@ -32,32 +39,54 @@ while [[ $# -gt 0 ]]; do
             exit 0
             ;;
         *)
-            echo "Unknown option: $1"
-            echo "Use --help for usage information"
-            exit 1
+            EXTRA_ARGS+=("$1")
+            shift
             ;;
     esac
 done
 
 trap 'echo Cleaning up...; kill 0' EXIT
 
-MODEL_NAME="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
+# Use TCP transport to avoid NATS payload limits for multimodal
+export DYN_REQUEST_PLANE=tcp
+
+# Configure model-specific args
+MODEL_SPECIFIC_ARGS=""
+if [[ "$MODEL_NAME" == "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8" ]]; then
+    MODEL_SPECIFIC_ARGS="--tensor-parallel-size=8 --max-model-len=208960 --gpu-memory-utilization 0.80"
+fi
 
 if [[ $HEAD_NODE -eq 1 ]]; then
     # run ingress
     # dynamo.frontend accepts either --http-port flag or DYN_HTTP_PORT env var (defaults to 8000)
     python -m dynamo.frontend &
 
-    # run processor
+    # run processor (CPU-only to avoid competing for GPU memory with workers)
+    CUDA_VISIBLE_DEVICES="" \
     python -m dynamo.vllm --multimodal-processor --enable-multimodal --model $MODEL_NAME &
 
-    # Llama 4 doesn't support image embedding input, so the prefill worker will also
-    # handle image encoding inline.
-    # run prefill worker
-    VLLM_NIXL_SIDE_CHANNEL_PORT=20097 python -m dynamo.vllm --multimodal-encode-prefill-worker --is-prefill-worker --enable-multimodal --model $MODEL_NAME --tensor-parallel-size=8 --max-model-len=208960 --gpu-memory-utilization 0.80 --kv-events-config '{"publisher":"zmq","topic":"kv-events","endpoint":"tcp://*:20080"}' &
+    # Prefill worker handles prompt processing and image encoding
+    # Uses all 8 GPUs for tensor-parallel
+    CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+    VLLM_NIXL_SIDE_CHANNEL_PORT=20097 \
+    python -m dynamo.vllm \
+        --enable-multimodal \
+        --model $MODEL_NAME \
+        --is-prefill-worker \
+        $MODEL_SPECIFIC_ARGS \
+        --kv-events-config '{"publisher":"zmq","topic":"kv-events","endpoint":"tcp://*:20080"}' \
+        "${EXTRA_ARGS[@]}" &
 else
     # run decode worker on non-head node
-    VLLM_NIXL_SIDE_CHANNEL_PORT=20098 python -m dynamo.vllm --multimodal-decode-worker --enable-multimodal --model $MODEL_NAME --tensor-parallel-size=8 --max-model-len=208960 --gpu-memory-utilization 0.80 --kv-events-config '{"publisher":"zmq","topic":"kv-events","endpoint":"tcp://*:20081"}' &
+    # Uses all 8 GPUs for tensor-parallel
+    CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+    VLLM_NIXL_SIDE_CHANNEL_PORT=20098 \
+    python -m dynamo.vllm \
+        --enable-multimodal \
+        --model $MODEL_NAME \
+        $MODEL_SPECIFIC_ARGS \
+        --kv-events-config '{"publisher":"zmq","topic":"kv-events","endpoint":"tcp://*:20081"}' \
+        "${EXTRA_ARGS[@]}" &
 fi
 
 # Wait for all background processes to complete