Local Models - Strix

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 is the easiest way to run local models on macOS, Linux, and Windows.

Setup

Install Ollama from ollama.ai
Pull a high-performance model:
bash
```
ollama pull qwen3-vl
```

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