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qualcomm/GenieX

Run frontier LLMs and VLMs locally on Qualcomm devices across NPU, GPU, and CPU with a few lines of code observed · 2026-08-28

github.com/qualcomm/GenieX · homepage · Rust · BSD-3-Clause (permissive) observed · 2026-08-28

Health v2 · maintenance only

85/100

  • Activity 99
  • Release rhythm 86
  • Longevity 53
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 2
  • age_days: 747
  • days_rel: 12
  • days_push: 7
  • n_releases_24m: 64

Full methodology

Adoption not part of the score

8326 stars · 1049 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

GenieX is an on-device generative AI inference runtime for Qualcomm Snapdragon devices, running frontier LLMs and VLMs on the Hexagon NPU, Adreno GPU, or CPU. It exposes a single C SDK through a CLI, Python and Kotlin/Java SDKs, Docker, and an OpenAI-compatible server, dispatching to llama.cpp (GGUF) or Qualcomm AI Engine Direct runtimes.

Use cases

  • run llama 3 or qwen3 locally on a snapdragon laptop npu
  • run gguf models from hugging face on hexagon npu
  • embed on-device llm inference in an android app
  • serve an openai-compatible local llm api on windows arm64
  • deploy vision-language models on qualcomm dragonwing iot devices
  • run llms offline on-device without cloud
  • benchmark npu vs gpu vs cpu inference on snapdragon

When to choose

  • you target Qualcomm Snapdragon hardware (Snapdragon X, 8 Elite, Dragonwing) and want NPU-accelerated local LLM/VLM inference
  • you want to run GGUF models from Hugging Face with minimal setup
  • you need an OpenAI-compatible local server or mobile (Kotlin) SDK for on-device AI
  • you want pre-compiled, chipset-optimized models from Qualcomm AI Hub

When to avoid

  • you run on non-Qualcomm hardware (Intel, AMD, Apple Silicon) - GenieX only works on Snapdragon
  • you need server-scale multi-GPU inference or high-concurrency production serving
  • you need non-GGUF model formats without a pre-compiled AI Hub bundle
  • you require a fully stable release - it is currently in developer preview

Facets

library · maturity active

llm-inference sdk cli http-server machine-learning large-language-models artificial-intelligence machine-learning developer-tools mobile-development iot windows python cli on-device-ai qualcomm snapdragon hexagon-npu gguf llama-cpp openai-compatible vlm edge-ai rust android linux docker arm64

4 sources

Member repositories

RepositoryRoleHealth v2
qualcomm/GenieXmain85

For agents

markdown · JSON · MCP: product_card(name="qualcomm/GenieX")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem