Implemented a cost-optimized AI infrastructure running on single RTX 4090 GPU with
automatic model switching based on request type. This enables text, image, and
music generation on the same hardware with sequential loading.
## New Components
**Model Orchestrator** (ai/model-orchestrator/):
- FastAPI service managing model lifecycle
- Automatic model detection and switching based on request type
- OpenAI-compatible API proxy for all models
- Simple YAML configuration for adding new models
- Docker SDK integration for service management
- Endpoints: /v1/chat/completions, /v1/images/generations, /v1/audio/generations
**Text Generation** (ai/vllm/):
- Reorganized existing vLLM server into proper structure
- Qwen 2.5 7B Instruct (14GB VRAM, ~50 tok/sec)
- Docker containerized with CUDA 12.4 support
**Image Generation** (ai/flux/):
- Flux.1 Schnell for fast, high-quality images
- 14GB VRAM, 4-5 sec per image
- OpenAI DALL-E compatible API
- Pre-built image: ghcr.io/matatonic/openedai-images-flux
**Music Generation** (ai/musicgen/):
- Meta's MusicGen Medium (facebook/musicgen-medium)
- Text-to-music generation (11GB VRAM)
- 60-90 seconds for 30s audio clips
- Custom FastAPI wrapper with AudioCraft
## Architecture
```
VPS (LiteLLM) → Tailscale VPN → GPU Orchestrator (Port 9000)
↓
┌───────────────┼───────────────┐
vLLM (8001) Flux (8002) MusicGen (8003)
[Only ONE active at a time - sequential loading]
```
## Configuration Files
- docker-compose.gpu.yaml: Main orchestration file for RunPod deployment
- model-orchestrator/models.yaml: Model registry (easy to add new models)
- .env.example: Environment variable template
- README.md: Comprehensive deployment and usage guide
## Updated Files
- litellm-config.yaml: Updated to route through orchestrator (port 9000)
- GPU_DEPLOYMENT_LOG.md: Documented multi-modal architecture
## Features
✅ Automatic model switching (30-120s latency)
✅ Cost-optimized single GPU deployment (~$0.50/hr vs ~$0.75/hr multi-GPU)
✅ Easy model addition via YAML configuration
✅ OpenAI-compatible APIs for all model types
✅ Centralized routing through LiteLLM proxy
✅ GPU memory safety (only one model loaded at time)
## Usage
Deploy to RunPod:
```bash
scp -r ai/* gpu-pivoine:/workspace/ai/
ssh gpu-pivoine "cd /workspace/ai && docker compose -f docker-compose.gpu.yaml up -d orchestrator"
```
Test models:
```bash
# Text
curl http://100.100.108.13:9000/v1/chat/completions -d '{"model":"qwen-2.5-7b","messages":[...]}'
# Image
curl http://100.100.108.13:9000/v1/images/generations -d '{"model":"flux-schnell","prompt":"..."}'
# Music
curl http://100.100.108.13:9000/v1/audio/generations -d '{"model":"musicgen-medium","prompt":"..."}'
```
All models available via Open WebUI at https://ai.pivoine.art
## Adding New Models
1. Add entry to models.yaml
2. Define Docker service in docker-compose.gpu.yaml
3. Restart orchestrator
That's it! The orchestrator automatically detects and manages the new model.
## Performance
| Model | VRAM | Startup | Speed |
|-------|------|---------|-------|
| Qwen 2.5 7B | 14GB | 120s | ~50 tok/sec |
| Flux.1 Schnell | 14GB | 60s | 4-5s/image |
| MusicGen Medium | 11GB | 45s | 60-90s for 30s audio |
Model switching overhead: 30-120 seconds
## License Notes
- vLLM: Apache 2.0
- Flux.1: Apache 2.0
- AudioCraft: MIT (code), CC-BY-NC (pre-trained weights - non-commercial)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Sebastian Krüger
3ed3e68271