# Qualcomm acquires Nexa AI, open-sources GenAI runtime for Hexagon NPUs

> Source: <https://github.com/qualcomm/GenieX>
> Published: 2026-07-07 22:44:11+00:00

GenieX is an **on-device Gen AI inference runtime for Qualcomm devices**. Bring almost any GGUF model from Hugging Face — or a pre-compiled bundle from [Qualcomm AI Hub](https://aihub.qualcomm.com/models/) — and run it locally on the **Hexagon NPU, Adreno GPU, or CPU** in a few lines of code. One C SDK underneath, exposed through a CLI, Python, Kotlin/Java, Docker, and an OpenAI-compatible server. It is the community version of Qualcomm GENIE.

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 |
|

**Android***(Mobile)*[Android SDK](#android-kotlin--java)**Linux ARM64***(IoT)*[CLI](#cli)·[Docker](#docker)·[Python](#python)No device on hand? Spin up a remote session on

[Qualcomm Device Cloud].

Pick your interface below. Each one follows the same three steps — **Install**, **Run**, and **Docs** — and shows both runtimes: a **GGUF** model from Hugging Face (`llama_cpp`

) and a **pre-compiled bundle** from Qualcomm AI Hub (`qairt`

, NPU).

**Install**

**Windows ARM64**—[download the installer](https://github.com/qualcomm/GenieX/releases), 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

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

📖 **Docs** — [Install](https://geniex.aihub.qualcomm.com/en/run/cli/install) · [Quickstart](https://geniex.aihub.qualcomm.com/en/run/cli/quickstart) · [Command reference](https://geniex.aihub.qualcomm.com/en/run/cli/reference)

**Install**

```
pip install geniex
```

**Run** — mirrors Hugging Face `transformers`

(`from_pretrained()`

→ `.generate()`

):

``` python
# 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](https://geniex.aihub.qualcomm.com/en/run/python/install) · [Quickstart](https://geniex.aihub.qualcomm.com/en/run/python/quickstart) · [API reference](https://geniex.aihub.qualcomm.com/en/run/python/api-reference)

**Install** — ships with the CLI ([install above](#cli)).

**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.

📖 **Docs** — [Local server guide](https://geniex.aihub.qualcomm.com/en/run/cli/local-server)

**Install** — add the SDK to your app module's `build.gradle.kts`

:

```
dependencies {
    implementation("com.qualcomm.qti:geniex-android:0.3.1")
}
```

**Run** — fastest path is the sample app (chat UI, model picker for GGUF + Qualcomm AI Hub bundles, VLM support):

The Android demo app lives in [ qualcomm/ai-hub-apps](https://github.com/qualcomm/ai-hub-apps/blob/release/geniex_chat_android/README.md). Clone it, open the sample app in Android Studio, and hit

**Run**.

📖 **Docs** — [Install](https://geniex.aihub.qualcomm.com/en/run/android/install) · [Quickstart](https://geniex.aihub.qualcomm.com/en/run/android/quickstart) · [API reference](https://geniex.aihub.qualcomm.com/en/run/android/api-reference)

**Install**

```
docker pull docker.io/qualcomm/geniex:latest
```

**Run** — the container wraps the CLI, so `geniex infer …`

works exactly as above.

📖 **Docs** — [Docker guide](https://geniex.aihub.qualcomm.com/en/run/linux/install)

**Install** — link against the single C header [ sdk/include/geniex.h](/qualcomm/GenieX/blob/main/sdk/include/geniex.h); every other interface is a thin wrapper over it.

📖 **Docs** — [sdk/README.md](/qualcomm/GenieX/blob/main/sdk/README.md) · [notes/build.md](/qualcomm/GenieX/blob/main/notes/build.md)

GenieX has two runtimes so you get **broad model coverage** *and* **peak Snapdragon performance** in one stack. Both LLMs and VLMs are supported.

llama.cpp (`llama_cpp` ) |
Qualcomm AI Engine Direct (`qairt` ) |
|
|---|---|---|
Get models from |
|

[Qualcomm AI Hub](https://aihub.qualcomm.com/models/)(pre-compiled)**Format****Compute units****Best for** For llama.cpp, pick the

precision when prompted — it has the best Hexagon NPU support. See the`Q4_0`

[Models guide →]for the full list, precisions, and how to run a local model.

Contributions are welcome! Before opening a PR, please read ** CONTRIBUTING.md** for branch naming, commit / PR title format, pre-commit checks, and the FFI-update rule for public SDK headers.

🏗️ Build the CLI, SDK, or Python bindings |
|

**Run**& select compute units / pull models[notes/run.md](/qualcomm/GenieX/blob/main/notes/run.md)** Release**— SemVer tags, channels, HTP signing[notes/release.md](/qualcomm/GenieX/blob/main/notes/release.md)** All developer docs**[docs/README.md](/qualcomm/GenieX/blob/main/docs/README.md)Questions, ideas, or want to show off what you built? Come say hi.

- 💬
— ask questions and chat with the community in real time.**Slack** - 🐛
— report a bug or request a feature.**GitHub Issues** - 🔗
— follow Qualcomm AI Hub for news and updates.**LinkedIn**

Thanks to everyone building GenieX 💙

BSD 3-Clause — see [LICENSE](/qualcomm/GenieX/blob/main/LICENSE) and [NOTICE](/qualcomm/GenieX/blob/main/NOTICE).

Use of this project is also subject to Qualcomm's [Terms of Use](https://www.qualcomm.com/site/terms-of-use).