Snapdragon platforms
GenieX is supported across three Snapdragon families — compute, mobile, and IoT — covering Windows ARM64, Android, and Linux ARM64.Supported chipsets
These are the chipsets GenieX is validated on. Each row lists the SoC identifier you’d see from auto-detection, and the AI Hub chipset id used when pulling Qualcomm AI Hub Models.Chipset vs. SoC id. Snapdragon X-series parts are identified by their Oryon CPU SKU (
X1E80100, X2E80100, …); every part within a generation shares one NPU architecture, so all X Elite SKUs map to the same AI Hub asset — including the X Plus and X2 Plus parts. Android reports its SoC through ro.soc.model, and Dragonwing boards through the device tree.On Android, GenieX passes the SoC id (e.g. SM8850) straight to Qualcomm AI Hub, which resolves it through its own alias table — GenieX deliberately keeps no second mapping. Pass the SoC id, not an AI Hub chipset name, and the right asset is selected. Variant suffixes are not exposed by ro.soc.model: a Galaxy S25 reports SM8750, not SM8750-AC (the alias for qualcomm-snapdragon-8-elite-for-galaxy), so pass the chipset explicitly if you need the variant asset.Interfaces per OS
The chipsets above are the validated set. GenieX may run on other Snapdragon parts within the same families — for everything Qualcomm AI Hub can compile for, see the Qualcomm AI Hub device list.
GenieX runtimes
GenieX ships with two runtimes so you get both broad model coverage and peak Snapdragon performance in one stack:llama_cpp— any GGUF model on Hugging Face, running on Hexagon NPU, Adreno GPU, or CPU through Qualcomm’s GGML Hexagon backend. The widest model selection.qairt(Qualcomm® AI Engine Direct) — pre-compiled bundles from Qualcomm AI Hub, compiled and quantized per chipset and pinned to the Hexagon NPU. The fastest path when your model is on Qualcomm AI Hub.
Qualcomm AI Engine Direct (also known as the Qualcomm AI Engine Direct SDK, Qualcomm AI Runtime, and historically QAIRT) is the official name. Throughout these docs we use the official name.
Pick
llama_cpp for any GGUF from Hugging Face, or when you need CPU/GPU fallback (e.g. IoT devices without HTP). Pick qairt for the fastest NPU path on models published to Qualcomm AI Hub.
Defaults
If you don’t pass a compute unit:
For llama.cpp’s HTP + CPU per-tensor scheduling (the faster path on Snapdragon), pass
hybrid explicitly.
llama.cpp
Thellama_cpp runtime executes any GGUF model through llama.cpp with Qualcomm’s GGML Hexagon backend. Pull any community GGUF from Hugging Face and run it on Snapdragon NPU, Adreno GPU, or pure CPU.
Compute units
--compute maps to the underlying hardware as follows:
The precision you pick at
geniex pull time also determines where the model lands — see Precisions (Quantizations) Supported.
Qualcomm AI Engine Direct
Theqairt runtime executes pre-compiled bundles from Qualcomm AI Hub through Qualcomm® AI Engine Direct. NPU-only, with the bundle compiled and quantized for a specific Snapdragon chipset — typically the fastest NPU path when your model is on Qualcomm AI Hub.
Compute units
Qualcomm AI Engine Direct is NPU only.Runtime constraints
The bundle has its precision, context length, and KV cache size baked in — none can be changed at runtime. On Android,nGpuLayers != 0 and nCtx != 0 are rejected with PARAM_NOT_SUPPORTED; leave both at defaults and tune max_tokens / enable_thinking only. To change precision or context length, get a different bundle from Qualcomm AI Hub.
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