---
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
```

### Gateways that require custom headers

Some OpenAI-compatible gateways require extra HTTP headers (for attribution or
tenant routing) alongside the bearer token. Set them with `LLM_EXTRA_HEADERS` as
a JSON object — they are sent on every request:

```bash
export STRIX_LLM="openai/your-model"
export LLM_API_BASE="https://your-gateway.example/v1"
export LLM_API_KEY="your-bearer-token"          # sent as Authorization: Bearer ...
export LLM_EXTRA_HEADERS='{"X-Feature-Key":"value","X-Tenant":"acme"}'
```

For endpoints behind a private CA, point Strix at your certificate bundle with
the standard `SSL_CERT_FILE=/path/to/ca-bundle.pem` — never disable TLS
verification against a real endpoint.

## Tool calling must return structured `tool_calls`

Strix is entirely tool-driven: every working turn must be a **native** function/tool call. If your inference server returns the tool call as plain assistant text instead of a structured `tool_calls` field, Strix never sees a call it can execute, so the agent makes no real progress — it re-prompts the model for a tool call and gives up once its recovery attempts are exhausted.

This is almost always an **inference-server configuration** problem, not a model or Strix problem. Common symptoms are the model printing a call as text such as:

```text
<tool_call>{"name": "exec_command", "arguments": {"cmd": "nmap ..."}}</tool_call>
exec_command(cmd="nmap ...", timeout=180)
{"action": "exec_command", "params": {"cmd": "nmap ..."}}
```

The fix belongs on the inference server: it must be configured to parse the model's tool tokens into structured `tool_calls`. A correctly configured endpoint either returns a structured call or rejects the request outright — it never leaks the call as text.

### Fixes by server

**llama.cpp (`llama-server`)**
- Run with `--jinja` and a correct tool-use chat template (`--chat-template` / `--chat-template-file` matching the model). Recent builds enable `--jinja` by default — **upgrade** if yours doesn't.
- For thinking models, align or disable reasoning (`--reasoning-format`, `-rea off`) so it doesn't break tool-call parsing.
- A low temperature (e.g. `--temp 0.2`) improves tool-call reliability.

**Ollama**
- Use a recent Ollama and a model whose template wires tools. Modern Ollama refuses tools (`tools param requires --jinja flag`) if the template lacks tool support.
- For reasoning models (e.g. qwen3), disable the model's **thinking** mode — thinking left on frequently pushes the tool call into the text `content` instead of the structured `tool_calls` field. Turn it off on the Ollama side (a non-thinking model variant, or `think: false` in the model's parameters / `Modelfile`).
- Raise **`num_ctx`** to at least 16k–32k. Strix sends a large system prompt plus many tool schemas; at Ollama's small default context the tool definitions are truncated out of the prompt and the model stops emitting valid calls. A short test prompt can look fine while a real scan fails, so set this explicitly rather than inferring it from a quick check.

**vLLM**
- Start with `--enable-auto-tool-choice`, a matching `--tool-call-parser` (`hermes`, `qwen3_xml`, or `llama3_json`), and a matching `--reasoning-parser` for reasoning models.

A low sampling temperature (roughly 0.2–0.6, depending on the family) also measurably reduces malformed tool calls on open-weight models. Set it on the server or in your model's parameters.

<Warning>
Even correctly configured, small models (< ~30B) emit malformed or text-form tool calls far more often than frontier models. Prefer a capable model for reliable agentic behavior.
</Warning>
