🤔 Windows + WSL2 + Ollama - which architecture should I use?

I’m setting up a local AI development environment on Windows + WSL2 and I’m trying to decide between two architectures.

Option 1 — Ollama/Models on Windows

        WSL2  
┌───────────────────┐
│  Application      │
│   ├── Python      │
│   ├── .venv       │
│   └── Source code │
└───────┬───────────┘
        │
 HTTP localhost:11434
        │
        ▼
     Windows 
┌───────────────┐
│    Ollama     │
│       ↓       │
│    Models     │
│       ↓       │
│      GPU      │
└───────────────┘

Option 2 — Ollama/Models inside WSL2

          WSL2
┌─────────────────────────┐
│       Application       │
│           ↓             │
│         Ollama          │
│           ↓             │
│         Models          │
└────────────┬────────────┘
             │  
        GPU access
             │ 
             ▼
          Windows
┌─────────────────────────┐
│      GPU / Driver       │
└─────────────────────────┘

My current setup is Option 1, and it works: WSL2 can access the Windows Ollama API through localhost:11434.

But I’m wondering if Option 2 is a better long-term architecture for local AI/LLM development.

I’m especially interested in:

🚀 Performance

🎮 GPU utilization

🧠 Model management

💾 Disk usage

🔧 Setup and maintenance

🐧 Linux/ML tooling

🐳 Docker integration

🌐 Networking

📈 Future scalability

If you use Ollama with Windows + WSL2, which architecture would you choose and why?

And if you've actually used both setups, I'd especially like to hear about your experience.

👇 Option 1 or Option 2?