What is GenieX

The easiest way to run frontier LLMs & VLMs locally on Qualcomm devices

Documentation · Quickstart · Models · Community

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GenieX is an on-device Gen AI inference runtime for Qualcomm devices. Bring almost any GGUF model from Hugging Face, ModelScope, or Docker Hub, or use a pre-compiled bundle from Qualcomm AI Hub. Run it locally on the Hexagon NPU, Adreno GPU, or CPU in a few lines of code. One C SDK underneath is exposed through a CLI, Python, Kotlin/Java, Docker, and an OpenAI-compatible server. It is the community version of Qualcomm GENIE.

Supported platforms

GenieX runs only on Qualcomm Snapdragon. Find your platform, then jump straight to the interface you want to use.

Platform	Example devices	Jump to a quickstart
🪟 Windows ARM64 (Compute)	Snapdragon X · X Elite	CLI · Python · Local server
🤖 Android (Mobile)	Snapdragon 8 Elite · 8 Elite Gen 5	Android SDK
🐧 Linux ARM64 (IoT)	Dragonwing QCS9075	CLI · Docker · Python

No device on hand? Spin up a remote session on Qualcomm Device Cloud.

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Quickstart

Pick your interface below. Each one follows the same three steps: Install, Run, and Docs. The examples show both runtimes: a GGUF model from Hugging Face, ModelScope, or Docker Hub (llama_cpp) and a pre-compiled bundle from Qualcomm AI Hub (qairt, NPU).

CLI

Install

• Windows ARM64 — download the installer, run it, then open a new terminal.
• Linux ARM64 — one line, no sudo:

  curl -fsSL https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-geniex/install.sh | sh
  

Run — chat with any model in one line (drag in an image for VLMs):

# GGUF from Hugging Face → llama.cpp (NPU / GPU / CPU)
geniex infer google/gemma-4-E4B-it-qat-q4_0-gguf

# GGUF from ModelScope → llama.cpp (NPU / GPU / CPU)
geniex infer https://modelscope.cn/models/Qwen/Qwen3-0.6B-GGUF

# Pre-compiled bundle from Qualcomm AI Hub → Qualcomm AI Engine Direct (NPU)
geniex infer ai-hub-models/Qwen2.5-VL-7B-Instruct

# GGUF from Docker Hub (https://hub.docker.com/u/ai) → llama.cpp (NPU / GPU / CPU)
geniex infer docker.io/ai/gemma3

📖 Docs — Install · Quickstart · Command reference

Python

Install

pip install geniex

Run — mirrors Hugging Face transformers (from_pretrained() → .generate()):

# GGUF from Hugging Face → llama.cpp
from geniex import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3.5-2B-GGUF", precision="Q4_0")

messages = [{"role": "user", "content": "What is 2+2?"}]
prompt = model.tokenizer.apply_chat_template(messages, add_generation_prompt=True)

for chunk in model.generate(prompt, max_new_tokens=256, stream=True):
    print(chunk, end="", flush=True)

model.close()

# Pre-compiled bundle from Qualcomm AI Hub → Qualcomm AI Engine Direct (NPU)
from geniex import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("ai-hub-models/Qwen3-4B")

messages = [{"role": "user", "content": "What is 2+2?"}]
prompt = model.tokenizer.apply_chat_template(messages, add_generation_prompt=True)

for chunk in model.generate(prompt, max_new_tokens=256, stream=True):
    print(chunk, end="", flush=True)

model.close()

📖 Docs — Install · Quickstart · API reference

OpenAI-compatible server

Install — ships with the CLI (install above).

Run — pull any model (GGUF or Qualcomm AI Hub bundle), then serve an OpenAI-compatible API:

geniex pull ai-hub-models/Qwen3-4B-Instruct-2507
geniex serve   # serves http://127.0.0.1:18181/v1

curl http://127.0.0.1:18181/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ai-hub-models/Qwen3-4B-Instruct-2507",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Point any OpenAI client at http://127.0.0.1:18181/v1 — no code changes.

This is the beginning of the README. Read the rest at the original. Read the full README ↗