This is a SpacemiT deployment package for the Qwen3-VL 30B-A3B mixture-of-experts vision-language model. The upstream model is from the Qwen Team and is designed for multimodal understanding across text, images, and video, including visual question answering, document understanding, OCR-style reading, coding, and agent workflows. See the Qwen3-VL Technical Report and the official Qwen3-VL announcement.

The package contains a Q4_1 GGUF text decoder and an ONNX vision encoder. The ONNX file is renamed to qwen3_vl_vision.onnx for a short, stable deployment name. The original Qwen3-VL model and this deployment package use the Apache-2.0 license; runtime dependency licenses remain with their upstream projects.

Install the SpacemiT ONNX Runtime release and build or unpack the SMT-enabled SpacemiT llama.cpp. Prebuilt RISC-V packages can be unpacked directly. To build llama.cpp from source, clone recursively, set RISCV_ROOT_PATH and SPACEMIT_ORT_DIR, then run:

On the board, set the runtime library path before starting the server:

K1 uses AI cores 0;1;2;3 and four threads. Use configs/K1/config.json and -t 4.

K3 uses AI cores 8;9;10;11;12;13;14;15 and eight threads. Use configs/K3/config.json and -t 8.

Do not interchange the configuration files: each ep_config pins the SpaceMIT Execution Provider to the correct board's AI cores.

K1:

K3 uses the same command with configs/K3 and -t 8.

Send an OpenAI-compatible POST /v1/chat/completions request containing an image data URL and a prompt such as Describe the image content.. Set temperature to 0, use a small max_tokens value for a smoke test, and disable Qwen thinking with chat_template_kwargs: {"enable_thinking": false}.

Example request:

The included humanspeech.jpg can be used for a basic end-to-end smoke test. Replace <BASE64_IMAGE> with its base64-encoded contents. The response should be HTTP 200 with a natural-language description; this is a functional check, not an accuracy benchmark.