Instructions to use nvidia/Nemotron-Cascade-2-30B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/Nemotron-Cascade-2-30B-A3B with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/Nemotron-Cascade-2-30B-A3B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-Cascade-2-30B-A3B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("nvidia/Nemotron-Cascade-2-30B-A3B", trust_remote_code=True)
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

vLLM

How to use nvidia/Nemotron-Cascade-2-30B-A3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/Nemotron-Cascade-2-30B-A3B"
# 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-Cascade-2-30B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/Nemotron-Cascade-2-30B-A3B

SGLang

How to use nvidia/Nemotron-Cascade-2-30B-A3B 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-Cascade-2-30B-A3B" \
    --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-Cascade-2-30B-A3B",
		"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-Cascade-2-30B-A3B" \
        --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-Cascade-2-30B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/Nemotron-Cascade-2-30B-A3B with Docker Model Runner:
```
docker model run hf.co/nvidia/Nemotron-Cascade-2-30B-A3B
```

Cascade-2 cheating attempts on Math is... cute

#25

by chankhavu - opened Apr 7

Discussion

chankhavu

Apr 7

•

edited Apr 7

I was doing some inference of this model on a subset of harder problems from Nemotron-Math-V2, hoping to get a decent dataset for fun. I noticed that this model created a bunch of unrelated files in the sandbox folder (which is not network isolated in my case foolishly):

It downloaded IMO2021SL.pdf, imo2022sl.pdf and so on. I opened those files and they were legitimate PDF files. Which means the model tried to cheat by downloading those files 😄 Here are the statistics of network access attempts (out of 10K reasoning traces):

Domain	Traces
artofproblemsolving.com	64
en.wikipedia.org	58
www.google.com	40
duckduckgo.com	39
oeis.org	28
math.stackexchange.com	13
api.stackexchange.com	12
www.imo-official.org	11
html.duckduckgo.com	11
raw.githubusercontent.com	8
api.duckduckgo.com	6
mathworld.wolfram.com	6
purplecomet.org	5
api.github.com	3
arxiv.org	3
www.bing.com	3
stackoverflow.com	3

The cutest part is that in most of these cheating attempts, it fails to produce the correct answer. Here are in-depth analysis together with exported reasoning traces with tool use: https://huggingface.co/datasets/chankhavu/nemotron-cascade2-cheating-attempts (analysis was done by Claude Code)

ychenNLP

NVIDIA org 26 days ago

•

edited 26 days ago

Thanks for the analysis. We did not use tool calls for IMO/IOI evaluation.

wping

NVIDIA org 26 days ago

@chankhavu
Thanks for providing the examples. We’ve observed similar behaviors before. We believe this stems from our SFT training data, generated by DeepSeek-V3.2 and GPT-OSS with tool calls, which includes trajectories involving network access attempts. Most of these are hallucinations and fail to produce correct answers.

Just to emphasize:
For benchmark evaluations , including IMO 2025 and IOI 2025, we disable tool calls and do not allow the model to access the network, to rule out any possibility of cheating attempts.

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