- Replace GPU_TAILSCALE_IP interpolation with GPU_VLLM_API_URL
- LiteLLM requires full URL in api_base with os.environ/ syntax
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Replace COMFYUI_BACKEND_HOST and SUPERVISOR_BACKEND_HOST with GPU_TAILSCALE_IP
- Update LiteLLM config to use os.environ/GPU_TAILSCALE_IP for vLLM models
- Add GPU_TAILSCALE_IP env var to LiteLLM service
- Configure qwen-2.5-7b and llama-3.1-8b to route through orchestrator
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Set drop_params: false in litellm_settings
- Set modify_params: false in litellm_settings
- Set drop_params: false in default_litellm_params
- Commented out LITELLM_DROP_PARAMS env var
- Removed --drop_params command flag
These settings were stripping critical streaming parameters causing
vLLM streaming responses to collapse into empty deltas
- Reverted direct orchestrator connection to WebUI
- Added stream: true parameter to qwen-2.5-7b model config
- Keep LiteLLM as single proxy for all models
- Changed back from hosted_vllm/qwen-2.5-7b to openai/qwen-2.5-7b
- Removed /v1 suffix from api_base (LiteLLM adds it automatically)
- Added supports_system_messages: false for vLLM compatibility
- Update api_base URLs from 100.100.108.13 to 100.121.199.88 (RunPod Tailscale IP)
- All self-hosted models (qwen-2.5-7b, flux-schnell, musicgen-medium) now route through Tailscale VPN
- Tested and verified connectivity between VPS and RunPod GPU orchestrator
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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>
This commit finalizes the GPU infrastructure deployment on RunPod:
- Added qwen-2.5-7b model to LiteLLM configuration
- Self-hosted on RunPod RTX 4090 GPU server
- Connected via Tailscale VPN (100.100.108.13:8000)
- OpenAI-compatible API endpoint
- Rate limits: 1000 RPM, 100k TPM
- Marked GPU deployment as COMPLETE in deployment log
- vLLM 0.6.4.post1 with custom AsyncLLMEngine server
- Qwen/Qwen2.5-7B-Instruct model (14.25 GB)
- 85% GPU memory utilization, 4096 context length
- Successfully integrated with Open WebUI at ai.pivoine.art
Infrastructure:
- Provider: RunPod Spot Instance (~$0.50/hr)
- GPU: NVIDIA RTX 4090 24GB
- Disk: 50GB local SSD + 922TB network volume
- VPN: Tailscale (replaces WireGuard due to RunPod UDP restrictions)
Model now visible and accessible in Open WebUI for end users.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Removed supports_prompt_caching parameter that was causing 400 errors.
Prompt caching is automatically enabled by Anthropic when the client
sends cache_control blocks in messages - no config needed.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Added supports_prompt_caching: true to all Claude models:
- claude-sonnet-4
- claude-sonnet-4.5
- claude-3-5-sonnet
- claude-3-opus
- claude-3-haiku
This enables Anthropic's prompt caching feature across all models,
significantly reducing latency and costs for repeated requests
with the same system prompts.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Configure LiteLLM to use existing Redis from core stack for caching:
- Enabled cache with Redis backend
- Set TTL to 1 hour for cached responses
- Uses core_redis container on default port
This will improve performance by caching API responses.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Disabled cache setting that requires Redis configuration.
Prompt caching at the Anthropic API level is still enabled
via supports_prompt_caching setting.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Reduce database logging overhead and enable prompt caching:
- Disabled verbose logging (set_verbose: false)
- Disabled spend tracking logs to reduce DB writes
- Disabled tag tracking and daily spend logs
- Removed success/failure callbacks
- Enabled prompt caching for claude-sonnet-4.5
- Set log level to ERROR only
- Removed --detailed_debug flag from command
This should significantly improve response times by eliminating
unnecessary database writes for every request.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
According to litellm docs, drop_params only drops OpenAI parameters.
Since prompt_cache_key is an Anthropic-specific parameter, we need
to use additional_drop_params to explicitly drop it.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Explicitly set drop_params and supports_prompt_caching=false for
claude-sonnet-4.5 model to prevent prompt_cache_key parameter from
being sent to Anthropic API.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Add router_settings and default_litellm_params to ensure unsupported
parameters like prompt_cache_key are properly dropped when using codex
with the litellm proxy.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Added disable_responses_id_security setting to allow Codex CLI to access
the /responses endpoint without 401 errors. This removes the encryption
requirement on response IDs while maintaining API key authentication.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Changed API key reference from ${ANTHROPIC_API_KEY} to
os.environ/ANTHROPIC_API_KEY to match LiteLLM's documented syntax.
The os.environ/ prefix tells LiteLLM to use os.getenv() to retrieve
the environment variable at runtime, which is the correct way to
reference environment variables in LiteLLM config files.
Reference: https://docs.litellm.ai/docs/proxy/deploy🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Added LiteLLM as an OpenAI-compatible proxy for Anthropic's API to
enable Claude models in Open WebUI.
**New Service: litellm**
- Image: ghcr.io/berriai/litellm:main-latest
- Internal proxy on port 4000
- Converts Anthropic API to OpenAI-compatible format
- Health check with 30s intervals
- Not exposed via Traefik (internal only)
**LiteLLM Configuration (litellm-config.yaml)**
- Claude Sonnet 4 (claude-sonnet-4-20250514)
- Claude Sonnet 4.5 (claude-sonnet-4-5-20250929)
- Claude 3.5 Sonnet (claude-3-5-sonnet-20241022)
- Claude 3 Opus (claude-3-opus-20240229)
- Claude 3 Haiku (claude-3-haiku-20240307)
**Open WebUI Configuration Updates**
- Changed OPENAI_API_BASE_URLS to point to LiteLLM proxy
- URL: http://litellm:4000/v1
- Added litellm as dependency for webui service
- Dummy API key for proxy authentication
**Why LiteLLM?**
Anthropic's API uses different endpoint structure and authentication
headers compared to OpenAI. LiteLLM acts as a translation layer,
allowing Open WebUI to use Claude models through its OpenAI-compatible
interface.
**Available Models in Open WebUI**
- claude-sonnet-4 (latest Claude Sonnet 4)
- claude-sonnet-4.5 (Claude Sonnet 4.5)
- claude-3-5-sonnet
- claude-3-opus
- claude-3-haiku
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
