Instructions to use RedHatAI/Qwen3.8-2.4T-A95B-NVFP4-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/Qwen3.8-2.4T-A95B-NVFP4-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/Qwen3.8-2.4T-A95B-NVFP4-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/Qwen3.8-2.4T-A95B-NVFP4-FP8")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/Qwen3.8-2.4T-A95B-NVFP4-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 RedHatAI/Qwen3.8-2.4T-A95B-NVFP4-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/Qwen3.8-2.4T-A95B-NVFP4-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": "RedHatAI/Qwen3.8-2.4T-A95B-NVFP4-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/Qwen3.8-2.4T-A95B-NVFP4-FP8

SGLang

How to use RedHatAI/Qwen3.8-2.4T-A95B-NVFP4-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 "RedHatAI/Qwen3.8-2.4T-A95B-NVFP4-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": "RedHatAI/Qwen3.8-2.4T-A95B-NVFP4-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 "RedHatAI/Qwen3.8-2.4T-A95B-NVFP4-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": "RedHatAI/Qwen3.8-2.4T-A95B-NVFP4-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/Qwen3.8-2.4T-A95B-NVFP4-FP8 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/Qwen3.8-2.4T-A95B-NVFP4-FP8
```

RedHatAI/Qwen3.8-2.4T-A95B-NVFP4-FP8

This is a quantized version of Qwen/Qwen3.8-2.4T-A95B with MoE layers quantized to NVFP4 and attention layers quantized to FP8 block

Usage

This model is intended for deployment with vLLM. You can serve the model using

vllm serve RedHatAI/Qwen3.8-2.4T-A95B-NVFP4 \
    --data-parallel-size 8 \
    --enable-expert-parallel 8 \
    --reasoning-parser qwen3 \
    --max-num-seqs 140

Creation Process

This model was quantized using LLM Compressor, see https://github.com/vllm-project/llm-compressor/blob/main/docs/key-models/qwen3.5/nvfp4-moe-example.md

Downloads last month: 5

Safetensors

Model size

1.4T params

Tensor type

BF16

F8_E4M3

Model tree for RedHatAI/Qwen3.8-2.4T-A95B-NVFP4-FP8

Base model

Qwen/Qwen3.8-2.4T-A95B

Quantized

(22)

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