> ## Documentation Index
> Fetch the complete documentation index at: https://geniex.aihub.qualcomm.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Quickstart
> Run your first model from the GenieX CLI.
## **Prerequisites**
* The CLI installed — see [Install](/en/run/cli/install).
* Interactive shell from container (Docker only) — see [Run interactively](/en/run/linux/install#run-interactively).
* Familiarity with [runtime choice](/en/get-started/platforms#geniex-runtimes) — `qairt` (Qualcomm AI Engine Direct) for Qualcomm AI Hub Models, `llama_cpp` for any GGUF.
## **Run your first model**
### **Qualcomm AI Engine Direct runtime (Qualcomm AI Hub)**
**Language model:**
```powershell windows theme={"dark"}
geniex infer ai-hub-models/Qwen3-4B
```
**Multimodal model:**
```powershell windows theme={"dark"}
geniex infer ai-hub-models/Qwen2.5-VL-7B-Instruct
```
### **llama.cpp runtime (GGUF)**
Pick `Q4_0` when prompted — it has the best Hexagon NPU support.
**Language model:**
```powershell windows theme={"dark"}
geniex infer unsloth/Qwen3.5-0.8B-GGUF
```
**Multimodal model:**
```powershell windows theme={"dark"}
geniex infer Qwen/Qwen3-VL-2B-Instruct-GGUF
```
When prompted:
* **Model type** — `vlm` for vision-language models, `llm` for text-only models. For `Qwen3.5` and `Gemma4`, pick `llm` for now (multimodal support coming soon).
* **Precision (Quantization)** — `Q4_0` for best Hexagon NPU performance.
To try other GGUF models, copy any compatible GGUF path from Hugging Face and substitute it into the command above. See [Run a GGUF model from Hugging Face](/en/models/supported#run-a-gguf-model-from-hugging-face).
## **Run a local model**
Already have a model on disk, or want to self-convert a bundle from Hugging Face? Use `geniex pull` with `--local-path` to register it, then run it like any other model. See:
* [Run a local Qualcomm AI Engine Direct bundle](/en/models/supported#run-a-local-qualcomm-ai-engine-direct-bundle) — self-converted from Hugging Face, an extracted bundle directory, or an AI Hub `.zip`.
* [Run a local GGUF model](/en/models/supported#run-a-local-gguf-model) — a directory containing your `.gguf` file.
## **Next steps**
Expose an OpenAI-compatible HTTP API on `localhost:18181`.
Every command, every flag.