# Windows Setup — Local LLM Stack

> **Two scripts. Zero IDE required. Fresh machine to running dashboard in ~30 minutes.**

## Quick Start

### Step 1: Windows Side (PowerShell as Admin)

```powershell
# Allow script execution for this session
Set-ExecutionPolicy -Scope Process Bypass

# Run the Windows setup (installs Ollama, pulls models, installs WSL2)
.\setup-windows.ps1
```

**What it does:** Verifies NVIDIA drivers, installs Ollama, pulls 5 models (~52 GB), installs WSL2 Ubuntu 24.04.

> After WSL2 install you may need to **reboot**. Ubuntu will ask for a username/password on first launch.

### Step 2: WSL2 Side (Ubuntu terminal)

```bash
# One-liner — downloads and runs the WSL2 setup script
curl -fsSL https://raw.githubusercontent.com/saravanakumardb1/learning_ai_common_plat/main/__LOCAL_LLMs/windows_specific/setup-wsl.sh | bash
```

**What it does:** Installs Node.js, Python, ffmpeg, cmake → builds Whisper.cpp with CUDA → sets up TTS → starts the dashboard.

### Step 3: Open Browser

```
http://localhost:3000
```

Dashboard should show all green. Done.

---

## What Gets Installed

| Component          | Where                              | Size          |
| ------------------ | ---------------------------------- | ------------- |
| NVIDIA drivers     | Windows                            | pre-installed |
| Ollama             | Windows (native)                   | ~200 MB       |
| 5 LLM models       | Windows (`%USERPROFILE%\.ollama\`) | ~52 GB        |
| WSL2 Ubuntu 24.04  | Windows                            | ~1 GB         |
| Node.js 20 LTS     | WSL2                               | ~50 MB        |
| Python 3.12 + venv | WSL2                               | ~200 MB       |
| whisper.cpp (CUDA) | WSL2 `/usr/local/bin/`             | ~50 MB        |
| Whisper model      | WSL2 `~/whisper-models/`           | ~1.5 GB       |
| SNAC decoder       | WSL2 (repo `models/`)              | ~76 MB        |
| Qwen3-TTS 0.6B     | WSL2 (repo `models/`)              | ~1.7 GB       |
| PyTorch CUDA       | WSL2 (`.venv-qwen-tts/`)           | ~2.5 GB       |
| Dashboard deps     | WSL2 (`dashboard/node_modules/`)   | ~200 MB       |

**Total:** ~60 GB (mostly Ollama models)

---

## Machines

| Machine                       | Hostname    | Role                               | Spec File                                              |
| ----------------------------- | ----------- | ---------------------------------- | ------------------------------------------------------ |
| **Razer Blade 18** (RTX 5090) | —           | ML powerhouse, GPU inference       | [razer-blade-18-spec.md](razer-blade-18-spec.md)       |
| **HP Z240 Tower** (i7-7700K)  | bl1box      | Always-on server, OpenClaw Gateway | [hp-z240-windows-spec.md](hp-z240-windows-spec.md)     |
| **Dell P16s** (Ryzen 7 PRO)   | WIN-6TAK... | Portable workstation               | [dell-P16s-windows-spec.md](dell-P16s-windows-spec.md) |

## Files in This Directory

| File                           | Purpose                                       |
| ------------------------------ | --------------------------------------------- |
| **README.md**                  | This file — quick start guide                 |
| **setup-windows.ps1**          | PowerShell script — Windows-side setup        |
| **setup-wsl.sh**               | Bash script — WSL2-side setup                 |
| **setup-guide.md**             | Detailed manual guide with troubleshooting    |
| **razer-blade-18-spec.md**     | Full hardware specs for the Razer Blade 18    |
| **hp-z240-windows-spec.md**    | HP Z240 spec + OpenClaw server use case guide |
| **dell-P16s-windows-spec.md**  | Dell P16s system info                         |
| **all-machines-comparison.md** | All 4 machines side-by-side comparison        |
| **capabilities/**              | 7 deep-dive GPU capability guides             |

## Related: OpenClaw AI Assistant

The HP Z240 is the recommended always-on host for [OpenClaw](https://github.com/openclaw/openclaw) — a self-hosted AI assistant that connects to WhatsApp, Telegram, Slack, Discord, and more.

| File                                                                                               | Purpose                                       |
| -------------------------------------------------------------------------------------------------- | --------------------------------------------- |
| [`../OPEN_CLAW/SETUP_GUIDE.md`](../OPEN_CLAW/SETUP_GUIDE.md)                                       | Step-by-step install + secure setup guide     |
| [`../OPEN_CLAW/openclaw-personal-ai-assistant.md`](../OPEN_CLAW/openclaw-personal-ai-assistant.md) | Reference doc — features, security, tips      |
| [`../OPEN_CLAW/validate-security.sh`](../OPEN_CLAW/validate-security.sh)                           | Security validation script (run post-install) |

---

## After Setup

```bash
# Daily usage — start everything
cd ~/code/mygh/learning_ai_common_plat/__LOCAL_LLMs
bash start-dashboard.sh

# Check status
bash start-dashboard.sh status

# Stop
bash start-dashboard.sh stop

# Test TTS
.venv-qwen-tts/bin/python test_orpheus_tts.py
.venv-qwen-tts/bin/python test_qwen_tts.py
```

## Troubleshooting

See [setup-guide.md](setup-guide.md#troubleshooting) for common issues:

- Ollama not accessible from WSL2
- CUDA not visible in WSL2
- Slow filesystem performance