This is Qwen3-Coder-30B-A3B-Instruct model converted to the OpenVINO™ IR (Intermediate Representation) format with weights compressed to INT4 by NNCF.

Quantization Parameters

The quantization was performed using optimum-cli export openvino with the following parameters:

mode: INT4_ASYM
group_size: 128

For more information on quantization, check the OpenVINO model optimization guide.

Compatibility

The provided OpenVINO™ IR model is compatible with:

OpenVINO version 2026.0.0 and higher
Optimum Intel 1.27.0 and higher

Running Model Inference with Optimum Intel

Install packages required for using Optimum Intel integration with the OpenVINO backend:

pip install optimum[openvino]

Run model inference:

from transformers import AutoTokenizer
from optimum.intel.openvino import OVModelForCausalLM

model_id = "OpenVINO/Qwen3-Coder-30B-A3B-Instruct-int4-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForCausalLM.from_pretrained(model_id)

inputs = tokenizer("What is OpenVINO?", return_tensors="pt")

outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)

For more examples and possible optimizations, refer to the Inference with Optimum Intel.

Running Model Inference with OpenVINO GenAI

Install packages required for using OpenVINO GenAI.

pip install openvino-genai huggingface_hub

Download model from HuggingFace Hub

import huggingface_hub as hf_hub

model_id = "OpenVINO/Qwen3-Coder-30B-A3B-Instruct-int4-ov"
model_path = "Qwen3-Coder-30B-A3B-Instruct-int4-ov"

hf_hub.snapshot_download(model_id, local_dir=model_path)

Run model inference:

import openvino_genai as ov_genai

device = "CPU"
pipe = ov_genai.LLMPipeline(model_path, device)
print(pipe.generate("What is OpenVINO?", max_length=200))

More GenAI usage examples can be found in OpenVINO GenAI library docs and samples

You can find more detaild usage examples in OpenVINO Notebooks:

Running Model with OpenAI client and OpenVINO Model Server

1a. Deploy model on Windows using binary package:

ovms.exe --rest_port 8000 --source_model OpenVINO/Qwen3-Coder-30B-A3B-Instruct-int4-ov --model_repository_path models --tool_parser qwen3coder --target_device GPU --cache_size 2 --task text_generation

1b. Deploy model in a Docker container:

docker run -d --user $(id -u):$(id -g) --rm -p 8000:8000 -v $(pwd)/models:/models --device /dev/dri --group-add=$(stat -c "%g" /dev/dri/render* | head -n 1) openvino/model_server:latest-gpu \
--rest_port 8000 --model_repository_path /models --source_model OpenVINO/Qwen3-Coder-30B-A3B-Instruct-int4-ov --tool_parser qwen3coder --target_device GPU --task text_generation

Install the client library:

pip install openai

Run the client:

from openai import OpenAI
client = OpenAI(
  base_url="http://localhost:8000/v3",
  api_key="unused"
)

stream = client.chat.completions.create(
    model="OpenVINO/Qwen3-Coder-30B-A3B-Instruct-int4-ov",
    messages=[{"role": "user", "content": "Hello."}],
    stream=True,
    tools=[],
)
for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

Also check how to use this model in an agentic flow with function calling, as shown in the agentic demo.

Limitations

Check the original model card for limitations.

Legal information

The original model is distributed under Apache License Version 2.0 license. More details can be found in Qwen3-Coder-30B-A3B-Instruct.

Disclaimer

Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See Intel’s Global Human Rights Principles. Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.