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diff --git a/collaborativeagents/slurm/test_vllm_only.sh b/collaborativeagents/slurm/test_vllm_only.sh
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+++ b/collaborativeagents/slurm/test_vllm_only.sh
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+#!/bin/bash
+#SBATCH --job-name=vllm_only
+#SBATCH --account=bfqt-delta-gpu
+#SBATCH --partition=gpuA100x4
+#SBATCH --nodes=1
+#SBATCH --gpus-per-node=1
+#SBATCH --time=00:45:00
+#SBATCH --mem=64G
+#SBATCH --output=slurm/logs/vllm_only_%j.out
+#SBATCH --error=slurm/logs/vllm_only_%j.err
+
+# Test vLLM inference speed ONLY (skip transformers which OOMs)
+set -e
+
+cd /projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/collaborativeagents
+
+# Activate conda environment
+source /u/yurenh2/miniforge3/etc/profile.d/conda.sh
+conda activate eval
+
+# Install vLLM if not already installed (requires GPU node for CUDA compilation)
+if ! python -c "import vllm" 2>/dev/null; then
+    echo "Installing vLLM..."
+    pip install vllm --quiet
+fi
+
+export HF_HOME=/projects/bfqt/users/yurenh2/hf_cache/huggingface
+
+echo "=== Job Info ==="
+echo "Job ID: $SLURM_JOB_ID"
+echo "Node: $SLURM_NODELIST"
+date
+
+echo ""
+echo "=== GPU Info ==="
+nvidia-smi --query-gpu=index,name,memory.total,memory.free --format=csv
+
+MODEL_8B="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/models/llama-3.1-8b-instruct"
+PORT=8003
+
+echo ""
+echo "============================================"
+echo "Starting vLLM Server for 8B Model"
+echo "============================================"
+
+# Start vLLM server with memory optimization
+python -m vllm.entrypoints.openai.api_server \
+    --model $MODEL_8B \
+    --port $PORT \
+    --gpu-memory-utilization 0.85 \
+    --max-model-len 4096 \
+    --disable-log-requests \
+    --dtype bfloat16 &
+SERVER_PID=$!
+echo "vLLM Server PID: $SERVER_PID"
+
+# Wait for server to be ready
+echo "Waiting for server to start..."
+for i in {1..90}; do
+    if curl -s http://localhost:$PORT/health > /dev/null 2>&1; then
+        echo "Server ready after $((i*2)) seconds"
+        break
+    fi
+    sleep 2
+done
+
+# Check if server is up
+if ! curl -s http://localhost:$PORT/health > /dev/null 2>&1; then
+    echo "ERROR: vLLM server failed to start"
+    cat slurm/logs/vllm_only_${SLURM_JOB_ID}.err | tail -50
+    kill $SERVER_PID 2>/dev/null || true
+    exit 1
+fi
+
+# Get model info
+echo ""
+echo "=== vLLM Server Info ==="
+curl -s http://localhost:$PORT/v1/models | python -m json.tool 2>/dev/null || echo "Could not get model info"
+
+echo ""
+echo "============================================"
+echo "Test 1: vLLM Sequential (20 requests)"
+echo "============================================"
+python scripts/benchmark_inference.py --mode vllm --url http://localhost:$PORT/v1 -n 20
+
+echo ""
+echo "============================================"
+echo "Test 2: vLLM Sequential (50 requests)"
+echo "============================================"
+python scripts/benchmark_inference.py --mode vllm --url http://localhost:$PORT/v1 -n 50
+
+echo ""
+echo "============================================"
+echo "Test 3: vLLM Concurrent 4 workers (50 req)"
+echo "============================================"
+python scripts/benchmark_inference.py --mode vllm --url http://localhost:$PORT/v1 -n 50 --concurrent
+
+# Cleanup
+echo ""
+echo "Cleaning up..."
+kill $SERVER_PID 2>/dev/null || true
+wait $SERVER_PID 2>/dev/null || true
+
+echo ""
+echo "============================================"
+echo "BENCHMARK COMPLETE!"
+echo "============================================"
+echo ""
+echo "Key metrics to compare with paper:"
+echo "  - Paper: 2000 conversations/hour on H100x8"
+echo "  - Expected A100x1: ~200-500 conv/hr"
+echo "  - Our old code: ~20 conv/hr (100x slower)"
+echo ""
+echo "If vLLM shows good throughput, we need to update"
+echo "our experiment code to use vLLM instead of transformers."
+echo ""
+date