---
title: "Local Models"
description: "Run Strix with self-hosted LLMs for privacy and air-gapped testing"
---

Running Strix with local models allows for completely offline, privacy-first security assessments. Data never leaves your machine, making this ideal for sensitive internal networks or air-gapped environments.

## Privacy vs Performance

| Feature | Local Models | Cloud Models (GPT-5/Claude 4.5) |
|---------|--------------|--------------------------------|
| **Privacy** | 🔒 Data stays local | Data sent to provider |
| **Cost** | Free (hardware only) | Pay-per-token |
| **Reasoning** | Lower (struggles with agents) | State-of-the-art |
| **Setup** | Complex (GPU required) | Instant |

<Warning>
**Compatibility Note**: Strix relies on advanced agentic capabilities (tool use, multi-step planning, self-correction). Most local models, especially those under 70B parameters, struggle with these complex tasks.

For critical assessments, we strongly recommend using state-of-the-art cloud models like **Claude 4.5 Sonnet** or **GPT-5**. Use local models only when privacy is the absolute priority.
</Warning>

## Ollama

[Ollama](https://ollama.ai) is the easiest way to run local models on macOS, Linux, and Windows.

### Setup

1. Install Ollama from [ollama.ai](https://ollama.ai)
2. Pull a high-performance model:
   ```bash
   ollama pull qwen3-vl
   ```
3. Configure Strix:
   ```bash
   export STRIX_LLM="ollama/qwen3-vl"
   export LLM_API_BASE="http://localhost:11434"
   ```

### Recommended Models

We recommend these models for the best balance of reasoning and tool use:

**Recommended models:**
- **Qwen3 VL** (`ollama pull qwen3-vl`)
- **DeepSeek V3.1** (`ollama pull deepseek-v3.1`)
- **Devstral 2** (`ollama pull devstral-2`)

## LM Studio / OpenAI Compatible

If you use LM Studio, vLLM, or other runners:

```bash
export STRIX_LLM="openai/local-model"
export LLM_API_BASE="http://localhost:1234/v1"  # Adjust port as needed
```
