Instructions to use Qwen/Qwen3-Coder-Next-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen3-Coder-Next-FP8 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Qwen/Qwen3-Coder-Next-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Coder-Next-FP8")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Coder-Next-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Qwen/Qwen3-Coder-Next-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/Qwen3-Coder-Next-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qwen/Qwen3-Coder-Next-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Qwen/Qwen3-Coder-Next-FP8

SGLang

How to use Qwen/Qwen3-Coder-Next-FP8 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Qwen/Qwen3-Coder-Next-FP8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qwen/Qwen3-Coder-Next-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Qwen/Qwen3-Coder-Next-FP8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qwen/Qwen3-Coder-Next-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Qwen/Qwen3-Coder-Next-FP8 with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen3-Coder-Next-FP8
```

Tested DGX Spark setup for Qwen3-Coder-Next-FP8 with Continue/Cline

by ztolley - opened Mar 22

Discussion

ztolley

Mar 22

I wanted to share a tested DGX Spark setup for Qwen/Qwen3-Coder-Next-FP8 aimed at a private coding-assistant workflow in VS Code:

https://github.com/ztolley/dgx-spark-qwen3-coder-next-compose

This is a community-tested reference setup, not an official vendor certification.

Setup summary:

Main assistant: Qwen/Qwen3-Coder-Next-FP8
Main context: 32768 by default
40960 was also validated as feasible on this hardware
Autocomplete sidecar: Qwen/Qwen2.5-Coder-3B
Backend: Spark-tuned vLLM path
IDE workflow: Continue and Cline via local OpenAI-compatible endpoints

A few measured notes from the default setup:

main model uses about 88.8 GiB GPU memory
autocomplete uses about 11.0 GiB
repeated prompt with prefix caching: 2.37s
autocomplete short completion: 1.56s

I also tested Qwen/Qwen2.5-Coder-7B for autocomplete under vLLM. It fit on the box, but it was slower and not clearly better enough to replace 3B as the default.

The repo includes the compose stack, config examples, and the validation notes behind the defaults:
https://github.com/ztolley/dgx-spark-qwen3-coder-next-compose/blob/main/docs/validation-and-decisions.md

If the Qwen team or other users have suggestions for an even better deployment path for Qwen3-Coder-Next on Spark-class hardware, I’d be interested in comparing notes.

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