VLLM_SETUP_GUIDE.md

RunPod vLLM Template Setup for Ultravox

✅ Use Pre-built vLLM (No Docker Building!)

This guide uses RunPod's existing vLLM Docker image - just configure and deploy.

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🚀 Step-by-Step Setup (10 minutes)

Step 1: Open RunPod Console

🔗 Go to: https://www.runpod.io/console/serverless

Click: "+ New Endpoint"

Step 2: Select vLLM Template

Search for: "vLLM"

Select: "vLLM - Fast LLM Inference" (official RunPod template)

Step 3: Configure Ultravox Model

Endpoint Configuration:

Name: ultravox-vllm

Container Image: runpod/worker-vllm:stable (pre-built!)

GPU Type: RTX 4090 (24GB VRAM)

Container Disk: 40 GB

Environment Variables:

Click "Add Environment Variable" and add these:

Name	Value
MODEL_NAME	fixie-ai/ultravox-v0_2
HF_TOKEN	YOUR_HF_TOKEN_HERE
MAX_MODEL_LEN	4096
GPU_MEMORY_UTILIZATION	0.9
TRUST_REMOTE_CODE	true

Scaling:

Min Workers: 0
Max Workers: 3
Scale Down Delay: 600 (10 minutes)

Step 4: Deploy

Click: "Deploy"

Wait: ~3-5 minutes for deployment

Status: Should show "Running" with green indicator

Copy: The Endpoint ID (looks like: abc123def456)

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🧪 Test Your Endpoint

Quick Test in RunPod Console

1. Go to your endpoint
2. Click "Requests" tab
3. Click "Send Test Request"
4. Use this payload:

{
  "input": {
    "prompt": "Hello! How are you today?",
    "max_tokens": 100,
    "temperature": 0.7
  }
}

5. Click "Run"
6. Wait ~8-12 seconds (cold start)
7. Should return text response

Test from Command Line

export RUNPOD_ENDPOINT_ID="your-endpoint-id-here"

python3 << 'EOF'
import runpod
import os

runpod.api_key = "YOUR_RUNPOD_API_KEY_HERE"
endpoint = runpod.Endpoint(os.getenv("RUNPOD_ENDPOINT_ID"))

result = endpoint.run_sync({
    "input": {
        "prompt": "What is artificial intelligence?",
        "max_tokens": 100
    }
}, timeout=60)

print("Response:", result)
EOF

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⚙️ Configure Our Service

Once you have the Endpoint ID, update the config:

# SSH to server
ssh -p 33337 root@136.59.129.136

# Edit config
nano /workspace/ultravox-pipeline/config/runpod.yaml

Add:

endpoints:
  ultravox:
    endpoint_id: "YOUR_ENDPOINT_ID_HERE"  # Paste it here
    gpu: "RTX_4090"
    min_workers: 0
    max_workers: 3

Save and exit: Ctrl+X, Y, Enter

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🎯 Test from Service

cd /workspace/ultravox-pipeline/src/services/runpod_llm

# Set environment
export RUNPOD_API_KEY="YOUR_RUNPOD_API_KEY_HERE"
export RUNPOD_ENDPOINT_ID="your-endpoint-id"

# Run service
python3 service.py &

# Test
curl -X POST http://localhost:8105/runpod/inference \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ultravox",
    "input": {
      "text": "Hello, world!"
    },
    "parameters": {
      "max_tokens": 50
    }
  }'

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📊 Expected Performance

Cold Start (First Request)

• Time: 8-15 seconds
• Why: Downloading model from HF → Loading into VRAM
• Frequency: Once per idle period (after scale-down)

Warm Inference

• Time: 0.3-0.8 seconds
• Throughput: ~30-50 tokens/second

Costs

• Price: $0.34/hour when active
• Testing: ~$0.01 for 1 hour of testing
• Production (2hr/day): ~$20/month

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🔧 Troubleshooting

Model not loading

• Check HF_TOKEN is set correctly
• Verify model name: fixie-ai/ultravox-v0_2
• Check logs in RunPod console

Out of memory

• Reduce MAX_MODEL_LEN to 2048
• Set GPU_MEMORY_UTILIZATION to 0.8

Slow cold starts

• Pre-download model (advanced)
• Use network storage (costs extra)

Connection timeout

• Increase timeout to 120 seconds
• Check endpoint is running (green status)

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⚠️ Important Notes

About vLLM + Ultravox:

• vLLM is primarily for text-only models
• Ultravox is multimodal (audio + text)
• vLLM will work for text input only
• For audio input, you need custom handler (Docker)

What works with vLLM:

• ✅ Text → Text (LLM inference)
• ❌ Audio → Text (needs custom handler)
• ❌ Text → Audio (needs TTS integration)

For full audio support:

• Use the custom Docker image (build.sh)
• Or process audio client-side (convert to text first)

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📝 Summary

1. ✅ No Docker building needed
2. ✅ Use RunPod's vLLM template
3. ✅ Set model name to fixie-ai/ultravox-v0_2
4. ✅ Add HF token for access
5. ⚠️ Text-only mode (no audio input/output)

For full speech-to-speech, build custom Docker image. For text-only testing, vLLM is perfect!

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Ready? Follow steps 1-4 above and paste your Endpoint ID here!