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
```

Cannot deploy with vllm on p4de.24xlarge with vllmV1 using --tensor-parallelization 8

by HenryGuillaumet - opened Feb 8

Discussion

HenryGuillaumet

Feb 8

Hello Team,

I am unable to deploy the FP8 model, it seems that the sharding does not work?
Anyone seeing it too?
Note that I am able to deploy the unquantized Qwen3-Coder-Next on the same instance without a problem

ERROR AM SEEING

Detected some but not all shards of model.layers.0.linear_attn.in_proj are quantized. All shards of fused layers to have the same precision.

Geodd

Feb 9

•

edited Feb 9

I have a question which might be unrelated, why 8 x A100, what sort of tokens capacity / tokens per second are you planning to process ?

HenryGuillaumet

Feb 10

Just trying to push the limit in terms of context size and speed generation, no specific goal in mind.

Geodd

Feb 10

@HenryGuillaumet
Its not possible to run fp8 on A100, you have to use blackwell, hopper or Ada GPUs
Example: H100, H200, L40s, B200

Do you mind if I ask questions : we are a new stack helping people run inferencing faster than vanilla deployment. A one sentence would be
open-weight inference: one-click deployment, automatic optimization, and reliable capacity so teams ship faster, pay less per outcome, and don’t think about infrastructure.

Is this something you would care or solve a problem of yours ?

HenryGuillaumet

Feb 10

Thanks for your answer, however it is possible to run FP8 on A100, it falls back to Marlin which is less optimized but definitely possible as I was able to run other FP8 models.

I am not interested thanks.

Geodd

Feb 11

•

edited Feb 11

Hi @HenryGuillaumet
I ran on 2x A100 and I did not have any issue, you were right about Marlin kernels and it did fallback but with a warning that heavy tasks maybe slower.
Speed is around 15 tokens a second, will be lower on larger context length.

CUDA_VISIBLE_DEVICES=0,1 vllm serve Qwen/Qwen3-Coder-Next-FP8 \
  --tensor-parallel-size 2 \
  --max-num-seqs 400 \
  --max-model-len 15000 \
  --disable-custom-all-reduce

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