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 Settings

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
```

Official quantizations?

by wijjjj - opened Mar 24

Discussion

wijjjj

Mar 24

So far I had very little success with any of the available quantizations done by the community. I don't know if it was just a bad choice of what layers to compress, or if there is something else wrong with this.

So I'm asking, are there any quanitzaitons planned? Total model size down to 30-35 GB would be fine to me.

Best regards.

chankhavu

Mar 24

•

edited Mar 24

@wijjjj , please try out these:

Used the same selective quantization recipe from the Nemotron 3 Nano Technical Report (Section 4).

Benchmarks

Calculated using NVIDIA-NeMo/Evaluator with config from Nemotron-3-Super-120B's eval config. Inference via vLLM with --mamba_ssm_cache_dtype float32 (see this discussion for more details).

Benchmark	BF16 (reproduced)	FP8	NVFP4
AIME 2025 (avg@8)	98.8	96.7	97.9
AIME 2026 (avg@8)	94.2	95.0	92.1
HMMT Feb 2025 (avg@8)	92.9	93.8	90.1

With 8 rollouts per problem, ±2% deviation across runs is expected. FP8 is equivalent to BF16. NVFP4 is consistently 1-2% below BF16.

wijjjj

Mar 26

Thanks @chankhavu , I will check it out!

michaelw9999

Mar 27

If anyone wants to try my quant of this one, I made an NVFP4 version for llama.cpp https://huggingface.co/michaelw9999/Nemotron-Cascade-2-30B-A3B-NVFP4-GGUF , did this using my own quantizer, I did not benchmark it on anything yet - feedback welcome!

ychenNLP

NVIDIA org Mar 27

check out this setup from Sudo su:

"i pointed hermes agent at nvidia's nemotron cascade 2 30B-A3B on a single RTX 3090 24GB. IQ4_XS quant by bartowski, 187 tok/s, 625K context. had it discover its own hardware, create an identity file, then build a full GPU marketplace UI from a single prompt."

https://x.com/sudoingX/status/2037512256599306578?s=20

wijjjj

Mar 27

@chankhavu , I can't get it running. Unfortunately FP8 GEMM is broken for Blackwell architecture. :( but thanks anyways sharing.

chankhavu

Mar 28

•

edited Mar 28

@wijjjj I tested it on RTX Pro 6000 (Blackwell), using vLLM. Here is the full command I used:

vllm serve chankhavu/Nemotron-Cascade-2-30B-A3B-FP8 \
--max-model-len 262144 \
--trust-remote-code \
--mamba_ssm_cache_dtype float32 \
--no-enable-prefix-caching \
--enable-auto-tool-choice \
--tool-call-parser qwen3_coder

The NVFP4 model needs additional flags:

export VLLM_USE_FLASHINFER_MOE_FP4=1
export VLLM_FLASHINFER_MOE_BACKEND=throughput
vllm serve chankhavu/Nemotron-Cascade-2-30B-A3B-NVFP4 \
--max-model-len 262144 \
--trust-remote-code \
--mamba_ssm_cache_dtype float32 \
--no-enable-prefix-caching \
--enable-auto-tool-choice \
--tool-call-parser qwen3_coder \
--kv-cache-dtype fp8

SGLang doesn't really work and I don't know why

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