Instructions to use nvidia/Nemotron-Labs-Diffusion-8B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/Nemotron-Labs-Diffusion-8B-Base with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/Nemotron-Labs-Diffusion-8B-Base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("nvidia/Nemotron-Labs-Diffusion-8B-Base", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nvidia/Nemotron-Labs-Diffusion-8B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/Nemotron-Labs-Diffusion-8B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Nemotron-Labs-Diffusion-8B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/Nemotron-Labs-Diffusion-8B-Base

SGLang

How to use nvidia/Nemotron-Labs-Diffusion-8B-Base 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 "nvidia/Nemotron-Labs-Diffusion-8B-Base" \
    --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": "nvidia/Nemotron-Labs-Diffusion-8B-Base",
		"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 "nvidia/Nemotron-Labs-Diffusion-8B-Base" \
        --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": "nvidia/Nemotron-Labs-Diffusion-8B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/Nemotron-Labs-Diffusion-8B-Base with Docker Model Runner:
```
docker model run hf.co/nvidia/Nemotron-Labs-Diffusion-8B-Base
```

YongganFu commited on 3 days ago

Commit

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@@ -14,12 +14,15 @@ tags:
 <div align="center" style="line-height: 1;">
-<a href="https://d1qx31qr3h6wln.cloudfront.net/publications/Nemotron_Diffusion_Tech_Report_v1.pdf?VersionId=db8_EMO8B.vmU26.jr7Le9pN3MqcUDNL" target="_blank" style="margin: 2px;">
     <img alt="Chat" src="https://img.shields.io/badge/📝Paper-Read Now!-536af5?color=76B900&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
 </a>
 <a href="https://huggingface.co/collections/nvidia/nemotron-labs-diffusion" target="_blank" style="margin: 2px;">
     <img alt="Nemotron-Labs-Diffusion Model Family" src="https://img.shields.io/badge/%F0%9F%A4%97-Nemotron--Labs--Diffusion_Model_Family-76B900" style="display: inline-block; vertical-align: middle;"/>
 </a>
 <a href="https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/" style="margin: 2px;">
   <img alt="License" src="https://img.shields.io/badge/License-NVIDIA Open Model License-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
 </a>
@@ -152,7 +155,7 @@ Please report model quality, risk, security vulnerabilities or NVIDIA AI Concern
 ```bibtex
 @techreport{fu2026nemotronlabsdiffusion,
   title       = {Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding},
-  author      = {Yonggan Fu and Lexington Whalen and Abhinav Garg and Chengyue Wu and Maksim Khadkevich and Nicolai Oswald and Enze Xie and Daniel Egert and Sharath Turuvekere Sreenivas and Shizhe Diao and Chenhan Yu and Ye Yu and Weijia Chen and Sajad Norouzi and Shiyi Lan and Ligeng Zhu and Jin Wang and Jindong Jiang and Morteza Mardani and Mehran Maghoumi and Song Han and Ante Jukic and Nima Tajbakhsh and Jan Kautz and Pavlo Molchanov},
   institution = {NVIDIA},
   year        = {2026},
   note        = {Technical report}

 <div align="center" style="line-height: 1;">
+<a href="https://d1qx31qr3h6wln.cloudfront.net/publications/Nemotron_Diffusion_Tech_Report.pdf?VersionId=1tm4XZATEzGV7cs51XAf.xmWupU20vYW" target="_blank" style="margin: 2px;">
     <img alt="Chat" src="https://img.shields.io/badge/📝Paper-Read Now!-536af5?color=76B900&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
 </a>
 <a href="https://huggingface.co/collections/nvidia/nemotron-labs-diffusion" target="_blank" style="margin: 2px;">
     <img alt="Nemotron-Labs-Diffusion Model Family" src="https://img.shields.io/badge/%F0%9F%A4%97-Nemotron--Labs--Diffusion_Model_Family-76B900" style="display: inline-block; vertical-align: middle;"/>
 </a>
+<a href="https://github.com/NVlabs/Nemotron-Labs-Diffusion" target="_blank" style="margin: 2px;">
+    <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Github Repository-76B900?logo=github&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+</a>
 <a href="https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/" style="margin: 2px;">
   <img alt="License" src="https://img.shields.io/badge/License-NVIDIA Open Model License-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
 </a>
 ```bibtex
 @techreport{fu2026nemotronlabsdiffusion,
   title       = {Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding},
+  author      = {Yonggan Fu and Lexington Whalen and Abhinav Garg and Chengyue Wu and Maksim Khadkevich and Nicolai Oswald and Enze Xie and Daniel Egert and Sharath Turuvekere Sreenivas and Shizhe Diao and Chenhan Yu and Ye Yu and Weijia Chen and Sajad Norouzi and Jingyu Liu and Shiyi Lan and Ligeng Zhu and Jin Wang and Jindong Jiang and Morteza Mardani and Mehran Maghoumi and Song Han and Ante Jukic and Nima Tajbakhsh and Jan Kautz and Pavlo Molchanov},
   institution = {NVIDIA},
   year        = {2026},
   note        = {Technical report}