Instructions to use nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16")
model = AutoModelForCausalLM.from_pretrained("nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", 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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-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-Ultra-550B-A55B-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-Ultra-550B-A55B-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16

SGLang

How to use nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-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-Ultra-550B-A55B-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-Ultra-550B-A55B-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-Ultra-550B-A55B-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-Ultra-550B-A55B-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 with Docker Model Runner:
```
docker model run hf.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
```

Chris-Alexiuk commited on Jun 10

Commit

624ba92

verified ·

1 Parent(s): bb203b7

Update SWE-Bench Verified and Multilingual.

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -120,8 +120,8 @@ This model is ready for commercial and non-commercial use.
 | **Agentic** | | | | | | | |
 | Terminal Bench 2.1 | 56.4 | 55.5 | 59.3 | 67.2 | 49.9 | 49.2 | 54.2 |
 | GDPVal | 46.7 | 47.6 | 54.7 | 50.4 | 34.6 | 54.6 | 50.2 |
-| SWE-Bench Verified | 71.9 | 72.2 | 73.8 | 69.5 | 69.9 | 74.0 | 72.4 |
-| SWE-Bench Multilingual | 67.7 | 69.2 | 73.8 | 65.9 | 67.7 | 71.9 | 72.1 |
 | ProfBench (Search) | 56.0 | 52.0 | 46.0 | 56.0 | 53.0 | 59.9 | 57.0 |
 | PinchBench | 90.0 | 77.6 | 81.2 | 90.2 | 86.6 | 88.6 | 91.3 |
 | TauBench V3 | | | | | | | |

 | **Agentic** | | | | | | | |
 | Terminal Bench 2.1 | 56.4 | 55.5 | 59.3 | 67.2 | 49.9 | 49.2 | 54.2 |
 | GDPVal | 46.7 | 47.6 | 54.7 | 50.4 | 34.6 | 54.6 | 50.2 |
+| SWE-Bench Verified | 70.7 | 75.3 | 76.2 | 75.7 | 73.6 | 74.5 | 73.5 |
+| SWE-Bench Multilingual | 67.7 | 71.8 | 74.8 | 77.1 | 70.9 | 76.5 | 75.0 |
 | ProfBench (Search) | 56.0 | 52.0 | 46.0 | 56.0 | 53.0 | 59.9 | 57.0 |
 | PinchBench | 90.0 | 77.6 | 81.2 | 90.2 | 86.6 | 88.6 | 91.3 |
 | TauBench V3 | | | | | | | |