Instructions to use RedHatAI/Qwen3.8-2.4T-A95B-NVFP4 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 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")
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")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/Qwen3.8-2.4T-A95B-NVFP4", 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 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Qwen3.8-2.4T-A95B-NVFP4

Model Overview

Model Architecture: Qwen3_5MoeForCausalLM
- Input: Text
- Output: Text
Model Optimizations:
- Weight quantization: FP4
- Activation quantization: FP4
Release Date: 2026-08-12
Version: 1.0
Model Developers: RedHatAI

This model is a quantized version of Qwen/Qwen3.8-2.4T-A95B. It was evaluated on several tasks to assess its quality in comparison to the unquantized model.

Model Optimizations

This model was obtained by quantizing the MoE expert weights and activations of Qwen/Qwen3.8-2.4T-A95B to FP4 (NVFP4) data type, ready for inference with vLLM. Attention and non-quantized layers keep their original precision.

This optimization reduces the number of bits per parameter in the quantized MoE layers from 16 to 4, reducing the disk size and GPU memory requirements of those layers by approximately 75%.

Only the weights and activations of the linear operators in the MoE experts are quantized using LLM Compressor.

Deployment

vLLM Serving

vllm serve RedHatAI/Qwen3.8-2.4T-A95B-NVFP4 \
    --tensor-parallel-size 8 \
    --enable-expert-parallel 8 \
    --reasoning-parser qwen3

Creation

This model was created by applying LLM Compressor with the NVFP4 scheme, exported in compressed-tensors format.

Evaluation

This model was evaluated on GPQA Diamond and DeepSWE (v1.1), served with vLLM (OpenAI-compatible API). Recovery is computed against the unquantized model.

Accuracy

Category	Benchmark	Qwen/Qwen3.8-2.4T-A95B	RedHatAI/Qwen3.8-2.4T-A95B-NVFP4	Recovery
Reasoning	GPQA Diamond	92.6	92.9	100.32%
Coding	DeepSWE (v1.1)	56.6	56.6	100.00%

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Safetensors

Model size

1.4T params

Tensor type

BF16

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

Base model

Qwen/Qwen3.8-2.4T-A95B

Quantized

(32)

this model