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 — 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
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.
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, 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 · Quickstart · Command reference
Install
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
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.
📖 Docs — Local server guide
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. Clone it, open the sample app in Android Studio, and hit Run.
📖 Docs — Install · Quickstart · 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
Install — link against the single C header sdk/include/geniex.h; every other interface is a thin wrapper over it.
📖 Docs — sdk/README.md · 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
Hugging Face (any GGUF)
Qualcomm AI Hub (pre-compiled)
Format
GGUF
Per-chipset bundle
Compute units
NPU · GPU · CPU
NPU only
Best for
Bringing your own GGUF
Highest NPU performance
For llama.cpp, pick the Q4_0 precision when prompted — it has the best Hexagon NPU support. See the 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.
Questions, ideas, or want to show off what you built? Come say hi.
💬 Slack — ask questions and chat with the community in real time.
🐛 GitHub Issues — report a bug or request a feature.
🔗 LinkedIn — follow Qualcomm AI Hub for news and updates.
Thanks to everyone building GenieX 💙
BSD 3-Clause — see LICENSE and NOTICE.
Use of this project is also subject to Qualcomm’s Terms of Use.
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