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

Qualcomm acquired Nexa AI and open-sourced GenieX, an on-device GenAI inference runtime for its Hexagon NPUs, Adreno GPUs, and CPUs. The runtime supports GGUF models from Hugging Face and pre-compiled bundles from Qualcomm AI Hub, enabling local AI inference on Snapdragon devices via CLI, Python, Kotlin/Java, Docker, and an OpenAI-compatible server.

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 .