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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Run frontier LLMs and VLMs locally on Qualcomm devices across NPU, GPU, and CPU with a few lines of code
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<div align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="GenieX-Logo-Hor-1-White.png" /> <source media="(prefers-color-scheme: light)" srcset="GenieX-Logo-Hor-1-Black.png" /> <img src="GenieX-Logo-Hor-1-Black.png" width="420" alt="Qualcomm AI Hub GenieX" /> </picture> ### The easiest way to run frontier LLMs & VLMs locally on Qualcomm devices [](#) [](https://geniex.aihub.qualcomm.com) [](https://github.com/qualcomm/GenieX/releases) [](LICENSE) [](https://aihub.qualcomm.com/community/slack) [**Documentation**](https://geniex.aihub.qualcomm.com) · [**Quickstart**](#quickstart) · [**Models**](#models) · [**Community**](#-community--contact) </div> --- 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. <div align="center"> <img src="docs/Mintlify-image/geniex_arch_v2.png" width="820" alt="GenieX architecture: CLI, Python, Java, Docker, and OpenAI-compatible Serve interfaces sit on a single GenieX SDK, which dispatches to the llama.cpp runtime (GGML over CPU / GPU / Hexagon HTP kernels) or the Qualcomm AI Engine Direct runtime on the NPU — across Windows, Android, and Linux." /> </div> ## 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](#cli) · [Python](#python) · [Local server](#openai-compatible-server) | | 🤖 **Android** *(Mobile)* | Snapdragon 8 Elite · 8 Elite Gen 5 | [Android SDK](#android-kotlin--java) | | 🐧 **Linux ARM64** *(IoT)* | Dragonwing QCS9075 | [CLI](#cli) · [Docker](#docker) · [Python](#python) | > No device on hand? Spin up a remote session on [Qualcomm Device Cloud](https://qdc.qualcomm.com/). --- ## Quickstart 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). ### CLI   **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`: ```bash 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): ```bash # 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 # GGUF from Docker Hub (https://hub.docker.com/u/ai) → llama.cpp (NPU / GPU / CPU) geniex infer docker.i
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667
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Zack Li · Qualcomm Inc
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Paul Zhu
112
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AlexCHEN · Stanford University · United States
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Alan Zhu · Nexa AI
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zdc
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MAN$I VERMA
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Eric Curtin · Docker, Inc
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Ting Heng Nguan
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7255250288a1cfab, topic:llm
matched fp:7255250288a1cfab, topic:vlm