Instructions to use nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4"
# 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-Labs-3-Puzzle-75B-A9B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4

SGLang

How to use nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4 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-Labs-3-Puzzle-75B-A9B-NVFP4" \
    --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-Labs-3-Puzzle-75B-A9B-NVFP4",
		"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-Labs-3-Puzzle-75B-A9B-NVFP4" \
        --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-Labs-3-Puzzle-75B-A9B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4
```

tomer-nv commited on Jul 7

Commit

1d370e4

verified ·

1 Parent(s): c743c5e

Add technical report link

Browse files

Files changed (1) hide show

README.md +2 -0

README.md CHANGED Viewed

@@ -34,6 +34,8 @@ Nemotron-Labs-3-Puzzle-75B-A9B is a deployment-optimized large language model de
 The model employs a hybrid MoE architecture with interleaved Mamba, MoE, and Attention layers. Like Nemotron-3-Super, it supports Multi-Token Prediction (MTP) for faster text generation. Compared to its parent, Puzzle-75B-A9B reduces the model from 120.7B total / 12.8B active parameters to 75.3B total / 9.3B active parameters.
 Compared to Nemotron-3-Super, Puzzle-75B-A9B:
 * Achieves approximately 2× higher server throughput on a single 8×B200 node at matched user-throughput constraints,
 * Increases sustainable 1M-token single-H100 concurrency from 1 request to 8 requests,

 The model employs a hybrid MoE architecture with interleaved Mamba, MoE, and Attention layers. Like Nemotron-3-Super, it supports Multi-Token Prediction (MTP) for faster text generation. Compared to its parent, Puzzle-75B-A9B reduces the model from 120.7B total / 12.8B active parameters to 75.3B total / 9.3B active parameters.
+See the tech report for full training and compression details: [Nemotron-Labs-3-Puzzle-75B-A9B: Compressing Hybrid MoE LLMs](https://arxiv.org/abs/2607.04371).
 Compared to Nemotron-3-Super, Puzzle-75B-A9B:
 * Achieves approximately 2× higher server throughput on a single 8×B200 node at matched user-throughput constraints,
 * Increases sustainable 1M-token single-H100 concurrency from 1 request to 8 requests,