Instructions to use nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", trust_remote_code=True, 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16"
# 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/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16

SGLang

How to use nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 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/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16" \
    --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/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
		"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/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16" \
        --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/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 with Docker Model Runner:
```
docker model run hf.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
```

suhara commited on Jan 8

Commit

52469ad

verified ·

1 Parent(s): eb82b63

Add vLLM example w/ 1M context length (#38)

Browse files

- Add vLLM example w/ 1M context length (a7827f58c69fb9db6b791916cae7cc86bef763bf)

Files changed (1) hide show

README.md +21 -2

README.md CHANGED Viewed

@@ -315,11 +315,12 @@ Download the custom parser from the Hugging Face repository.
 wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py
 ```
-Launch a vLLM server using the custom parser. In this example, we use a context length of 256k. You can increase the context size up to 1M to support longer contexts.
 ```
 vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 \
- --max-num-seqs 8 \
   --tensor-parallel-size 1 \
   --max-model-len 262144 \
   --port 8000 \
@@ -330,6 +331,24 @@ vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 \
   --reasoning-parser nano_v3
 ```
 Here is an example client code for vLLM. By default, the endpoint has reasoning enabled. We recommend setting a high value (e.g., 10,000) for `max_tokens`.
 ```shell

 wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py
 ```
+Launch a vLLM server using the custom parser.
 ```
 vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 \
+  --served-model-name model \
+  --max-num-seqs 8 \
   --tensor-parallel-size 1 \
   --max-model-len 262144 \
   --port 8000 \
   --reasoning-parser nano_v3
 ```
+In the example above, we use a context length of 256k. You can increase the context size up to 1M to support longer contexts.
+To enable this, set the `VLLM_ALLOW_LONG_MAX_MODEL_LEN=1` environment variable as shown below:
+```
+VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \
+vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 \
+  --served-model-name model \
+  --max-num-seqs 8 \
+  --tensor-parallel-size 1 \
+  --max-model-len 1M \
+  --port 8000 \
+  --trust-remote-code \
+  --enable-auto-tool-choice \
+  --tool-call-parser qwen3_coder \
+  --reasoning-parser-plugin nano_v3_reasoning_parser.py \
+  --reasoning-parser nano_v3
+```
 Here is an example client code for vLLM. By default, the endpoint has reasoning enabled. We recommend setting a high value (e.g., 10,000) for `max_tokens`.
 ```shell