# One-line install (Linux/WSL)
curl -fsSL https://raw.githubusercontent.com/Light-Heart-Labs/DreamServer/main/dream-server/get-dream-server.sh | bashOr manually:
git clone https://github.com/Light-Heart-Labs/DreamServer.git
cd DreamServer/dream-server
./install.shThe installer auto-detects your GPU, picks the right model, generates secure passwords, and starts everything. Open http://localhost:3000 and start chatting.
By default, Dream Server uses bootstrap mode for instant gratification:
- Starts immediately with a tiny 1.5B model (downloads in <1 minute)
- You can start chatting within 2 minutes of running the installer
- The full model downloads in the background
- When ready, hot-swap to the full model with zero downtime
No more staring at download bars. Start playing immediately.
# Download and run
Invoke-WebRequest -Uri "https://raw.githubusercontent.com/Light-Heart-Labs/DreamServer/main/install.ps1" -OutFile install.ps1
.\install.ps1Windows installer checks prerequisites (WSL2, Docker, NVIDIA), then delegates to the Linux install path.
One installer. Full AI stack. Zero config.
| Component | Purpose | Port |
|---|---|---|
| llama-server | LLM inference engine with continuous batching | 8080 |
| Open WebUI | Beautiful chat interface with history & web search | 3000 |
| Dashboard | Real-time GPU metrics, service health, model management | 3001 |
| LiteLLM | Multi-model API gateway | 4000 |
| OpenClaw | Autonomous AI agent framework | 7860 |
| SearXNG | Self-hosted web search | 8888 |
| Perplexica | Deep research engine | 3004 |
| n8n | Workflow automation (400+ integrations) | 5678 |
| Qdrant | Vector database for RAG | 6333 |
| Whisper | Speech-to-text | 9000 |
| Kokoro | Text-to-speech | 8880 |
| ComfyUI | Image generation | 8188 |
| Privacy Shield | PII scrubbing proxy | 8085 |
The installer automatically detects your GPU and selects the optimal configuration:
| Tier | VRAM | Model | Example GPUs |
|---|---|---|---|
| Tier 1 | 8-11GB | qwen2.5-7b-instruct (Q4_K_M) | RTX 4060 Ti, RTX 3060 12GB |
| Tier 2 | 12-15GB | qwen2.5-14b-instruct (Q4_K_M) | RTX 3080 12GB, RTX 4070 Ti |
| Tier 3 | 16-23GB | qwen2.5-32b-instruct (Q4_K_M) | RTX 4090, RTX 3090, A5000 |
| Tier 4 | 24GB+ | qwen2.5-72b-instruct (Q4_K_M) | 2x RTX 4090, A100 |
| Tier | Unified Memory | Model | Hardware |
|---|---|---|---|
| SH_LARGE | 90GB+ | qwen3-coder-next (80B MoE) | Ryzen AI MAX+ 395 (96GB) |
| SH_COMPACT | 64-89GB | qwen3-30b-a3b (30B MoE) | Ryzen AI MAX+ 395 (64GB) |
All models auto-selected based on available VRAM. No manual configuration.
| Quickstart | Step-by-step install guide with troubleshooting |
| FAQ | Common questions, hardware advice, configuration |
| Changelog | Version history and release notes |
| Contributing | How to contribute to Dream Server |
| Architecture | Modular installer design deep dive |
| Extensions | How to add custom services |
DreamServer/
├── dream-server/ # v2.0.0 - Production-ready local AI stack
│ ├── install.sh # Linux/WSL installer
│ ├── docker-compose.*.yml
│ ├── installers/ # Modular installer (13 phases)
│ ├── extensions/ # Drop-in service integrations
│ └── docs/ # 30+ documentation files
│
├── install.sh # Root installer (delegates to dream-server/)
├── install.ps1 # Windows installer
│
└── archive/ # Legacy projects (reference only)
├── guardian/ # Process watchdog
├── memory-shepherd/ # Agent memory lifecycle
├── token-spy/ # API cost monitoring
└── docs/ # Historical documentation
Shipping: dream-server/ is the v2.0.0 release.
Archive: Legacy tools from the OpenClaw Collective development period.
- Modular installer: 2591-line monolith → 6 libraries + 13 phases
- Zero-config service discovery: Extensions auto-register via manifests
- AMD Strix Halo support: ROCm 6.3 with unified memory models
- Bootstrap mode: Chat in 2 minutes, upgrade later
- Comprehensive testing:
make gateruns lint + test + smoke + simulate - 30+ docs: Installation, troubleshooting, Windows guides, extensions
See dream-server/CHANGELOG.md for full release notes.
Apache 2.0 — Use it, modify it, ship it. See LICENSE.
Built by The Collective — Android-17, Todd, and friends