#!/bin/bash # SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Disaggregated serving on a single GPU (prefill + decode share memory). # GPUs: 1 (requires 16+ GB VRAM) # # Usage: ./disagg_same_gpu.sh [GPU_MEM_FRACTION] # GPU_MEM_FRACTION: Fraction of GPU memory to use per worker (default: 0.45) # Example: ./disagg_same_gpu.sh 0.45 # GPU memory fraction to use per worker (default: 0.45 = 45% each = 90% total for both workers) GPU_MEM_FRACTION="${1:-0.45}" # Check GPU memory before starting disaggregated mode on single GPU FREE_GPU_GB=$(python3 -c "import torch; print(torch.cuda.mem_get_info()[0]/1024**3)" 2>/dev/null) if [ $? -ne 0 ]; then echo "Error: Failed to check GPU memory. Is PyTorch with CUDA available?" exit 1 fi REQUIRED_GB=16 # Use Python for floating-point comparison to avoid bc dependency if python3 -c "import sys; sys.exit(0 if float('$FREE_GPU_GB') >= $REQUIRED_GB else 1)"; then echo "GPU memory check passed: ${FREE_GPU_GB}GB available (required: ${REQUIRED_GB}GB)" else echo "Error: Insufficient GPU memory. Required: ${REQUIRED_GB}GB, Available: ${FREE_GPU_GB}GB" echo "Please free up GPU memory before running disaggregated mode on single GPU." exit 1 fi # Setup cleanup trap cleanup() { echo "Cleaning up background processes..." kill $DYNAMO_PID $PREFILL_PID 2>/dev/null || true wait $DYNAMO_PID $PREFILL_PID 2>/dev/null || true echo "Cleanup complete." } trap cleanup EXIT INT TERM MODEL="Qwen/Qwen3-0.6B" HTTP_PORT="${DYN_HTTP_PORT:-8000}" echo "==========================================" echo "Launching Disaggregated (same GPU)" echo "==========================================" echo "Model: $MODEL" echo "Frontend: http://localhost:$HTTP_PORT" echo "GPU Mem: ${GPU_MEM_FRACTION} per worker" echo "==========================================" echo "" echo "Example test command:" echo "" echo " curl http://localhost:${HTTP_PORT}/v1/chat/completions \\" echo " -H 'Content-Type: application/json' \\" echo " -d '{" echo " \"model\": \"${MODEL}\"," echo " \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]," echo " \"max_tokens\": 32" echo " }'" echo "" echo "==========================================" # run ingress with KV router mode for disaggregated setup # dynamo.frontend accepts either --http-port flag or DYN_HTTP_PORT env var (defaults to 8000) python3 -m dynamo.frontend --router-mode kv & DYNAMO_PID=$! # run prefill worker with metrics on port 8081 DYN_SYSTEM_PORT=${DYN_SYSTEM_PORT1:-8081} \ python3 -m dynamo.sglang \ --model-path Qwen/Qwen3-0.6B \ --served-model-name Qwen/Qwen3-0.6B \ --page-size 16 \ --tp 1 \ --trust-remote-code \ --disaggregation-mode prefill \ --disaggregation-bootstrap-port 12345 \ --host 0.0.0.0 \ --disaggregation-transfer-backend nixl \ --mem-fraction-static ${GPU_MEM_FRACTION} \ --chunked-prefill-size 4096 \ --max-prefill-tokens 4096 \ --enable-memory-saver \ --delete-ckpt-after-loading \ --max-running-requests 2 \ --enable-metrics & PREFILL_PID=$! # Wait for prefill worker to initialize before starting decode worker # This prevents both workers from competing for GPU memory simultaneously, which can cause OOM. # The prefill worker needs time to: # 1. Load model weights and allocate its memory fraction # 2. Initialize KV cache with --delete-ckpt-after-loading to free checkpoint memory # 3. Register with NATS service discovery so decode worker can find it echo "Waiting for prefill worker to initialize..." sleep 5 # run decode worker with metrics on port 8082 (foreground) DYN_SYSTEM_PORT=${DYN_SYSTEM_PORT2:-8082} \ python3 -m dynamo.sglang \ --model-path Qwen/Qwen3-0.6B \ --served-model-name Qwen/Qwen3-0.6B \ --page-size 16 \ --tp 1 \ --trust-remote-code \ --disaggregation-mode decode \ --disaggregation-bootstrap-port 12345 \ --host 0.0.0.0 \ --disaggregation-transfer-backend nixl \ --mem-fraction-static ${GPU_MEM_FRACTION} \ --chunked-prefill-size 4096 \ --max-prefill-tokens 4096 \ --enable-memory-saver \ --delete-ckpt-after-loading \ --max-running-requests 2 \ --enable-metrics