Instructions to use Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="Qwen/Qwen3-Coder-480B-A35B-Instruct-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-480B-A35B-Instruct-FP8")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Qwen/Qwen3-Coder-480B-A35B-Instruct-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-480B-A35B-Instruct-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-480B-A35B-Instruct-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8

SGLang

How to use Qwen/Qwen3-Coder-480B-A35B-Instruct-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-480B-A35B-Instruct-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-480B-A35B-Instruct-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-480B-A35B-Instruct-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-480B-A35B-Instruct-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8 with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8
```

vllm启动失败

by chuzhenfang - opened Jul 23, 2025

Discussion

chuzhenfang

Jul 23, 2025

ValueError: The output_size of gate's and up's weight = 320 is not divisible by weight quantization block_n = 128.

oggnusooj

Jul 24, 2025

Using the "--enable-expert-parallel" option might help.

chuzhenfang

Jul 24, 2025

Using the "--enable-expert-parallel" option might help.

thanks! It's work. with tp=8, the output_size of gate's and up's weight is split to 320 from original 2560?

pratiknarola

Jul 25, 2025

I am getting same error with sglang. Can someone help ?

[2025-07-25 09:53:38 TP3] Scheduler hit an exception: Traceback (most recent call last):
File "/sgl-workspace/sglang/python/sglang/srt/managers/scheduler.py", line 2921, in run_scheduler_process
scheduler = Scheduler(server_args, port_args, gpu_id, tp_rank, pp_rank, dp_rank)
File "/sgl-workspace/sglang/python/sglang/srt/managers/scheduler.py", line 344, in init
self.tp_worker = TpWorkerClass(
File "/sgl-workspace/sglang/python/sglang/srt/managers/tp_worker_overlap_thread.py", line 66, in init
self.worker = TpModelWorker(
File "/sgl-workspace/sglang/python/sglang/srt/managers/tp_worker.py", line 81, in init
self.model_runner = ModelRunner(
File "/sgl-workspace/sglang/python/sglang/srt/model_executor/model_runner.py", line 234, in init
self.initialize(min_per_gpu_memory)
File "/sgl-workspace/sglang/python/sglang/srt/model_executor/model_runner.py", line 277, in initialize
self.load_model()
File "/sgl-workspace/sglang/python/sglang/srt/model_executor/model_runner.py", line 623, in load_model
self.model = get_model(
File "/sgl-workspace/sglang/python/sglang/srt/model_loader/init.py", line 22, in get_model
return loader.load_model(
File "/sgl-workspace/sglang/python/sglang/srt/model_loader/loader.py", line 432, in load_model
model = _initialize_model(
File "/sgl-workspace/sglang/python/sglang/srt/model_loader/loader.py", line 174, in _initialize_model
return model_class(
File "/sgl-workspace/sglang/python/sglang/srt/models/qwen3_moe.py", line 702, in init
self.model = Qwen3MoeModel(
File "/sgl-workspace/sglang/python/sglang/srt/models/qwen3_moe.py", line 680, in init
super().init(
File "/sgl-workspace/sglang/python/sglang/srt/models/qwen2_moe.py", line 430, in init
self.layers, self.start_layer, self.end_layer = make_layers(
File "/sgl-workspace/sglang/python/sglang/srt/utils.py", line 525, in make_layers
+ [
File "/sgl-workspace/sglang/python/sglang/srt/utils.py", line 526, in
maybe_offload_to_cpu(layer_fn(idx=idx, prefix=add_prefix(idx, prefix)))
File "/sgl-workspace/sglang/python/sglang/srt/models/qwen2_moe.py", line 432, in
lambda idx, prefix: decoder_layer_type(
File "/sgl-workspace/sglang/python/sglang/srt/models/qwen3_moe.py", line 558, in init
self.mlp = Qwen3MoeSparseMoeBlock(
File "/sgl-workspace/sglang/python/sglang/srt/models/qwen3_moe.py", line 110, in init
self.experts = get_moe_impl_class()(
File "/sgl-workspace/sglang/python/sglang/srt/layers/moe/fused_moe_triton/layer.py", line 148, in init
self.quant_method.create_weights(
File "/sgl-workspace/sglang/python/sglang/srt/layers/quantization/fp8.py", line 533, in create_weights
raise ValueError(
ValueError: The output_size of gate's and up's weight = 320 is not divisible by weight quantization block_n = 128.

SamuelBang

Jul 26, 2025

For sglang, use "--enable-ep-moe" may help.

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