Instructions to use RedHatAI/Qwen3-32B-FP8-dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/Qwen3-32B-FP8-dynamic with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/Qwen3-32B-FP8-dynamic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/Qwen3-32B-FP8-dynamic")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/Qwen3-32B-FP8-dynamic")
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 RedHatAI/Qwen3-32B-FP8-dynamic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/Qwen3-32B-FP8-dynamic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/Qwen3-32B-FP8-dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/Qwen3-32B-FP8-dynamic

SGLang

How to use RedHatAI/Qwen3-32B-FP8-dynamic 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 "RedHatAI/Qwen3-32B-FP8-dynamic" \
    --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": "RedHatAI/Qwen3-32B-FP8-dynamic",
		"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 "RedHatAI/Qwen3-32B-FP8-dynamic" \
        --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": "RedHatAI/Qwen3-32B-FP8-dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/Qwen3-32B-FP8-dynamic with Docker Model Runner:
```
docker model run hf.co/RedHatAI/Qwen3-32B-FP8-dynamic
```

What is the difference between Qwen/Qwen3-32B-FP8 and this quatinized model？

by traphix - opened May 3, 2025

Discussion

traphix

May 3, 2025

Any difference? Qwen/Qwen3-32B-FP8

niells

May 4, 2025

big thanks for this quantization - for whatever reason i was unable to run the FP8 version provided by qwen (was crashing with

ValueError("type fp8e4nv not supported in this architecture. The supported fp8 dtypes are ('fp8e4b15', 'fp8e5')")

However this one runs great in vLLM.

traphix

May 4, 2025

•

edited May 4, 2025

big thanks for this quantization - for whatever reason i was unable to run the FP8 version provided by qwen (was crashing with
ValueError("type fp8e4nv not supported in this architecture. The supported fp8 dtypes are ('fp8e4b15', 'fp8e5')")
However this one runs great in vLLM.

I have the same problem running on A800.

bash99

May 6, 2025

This one and FP8 both works on 4090, but this one is much faster than Qwen/Qwen3-32B-FP8.
This one can use some fast kernels with vllm or sglang.

alexmarques

Red Hat AI org May 6, 2025

This model was produced using llm-compressor and is compatible with fast vLLM kernels. It uses dynamic per-token quantization for activations and static per-channel quantization for weights. After inspecting Qwen/Qwen3-32B-FP8 it seems they use grouped quantization, which will lead to slightly different results. I am able to run with vLLM on H100, but I'm not 100% about support in other hardware. The overall accuracy is similar based on some benchmarks we ran.

alexmarques changed discussion status to closed May 6, 2025

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