Instructions to use RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16 with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16")
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 RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16

SGLang

How to use RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16 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 "RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16" \
    --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": "RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16",
		"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 "RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16" \
        --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": "RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16
```

NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16

Model Overview

Model Architecture: NemotronHForCausalLM
- Input: Text
- Output: Text
- Total Parameters: 550B
- Active Parameters: 55B
Model Optimizations:
- Weight quantization: INT4 (W4A16, group size 128)
Intended Use Cases:
- Reasoning and complex problem solving.
- Mathematics and science.
- Code generation.
- Instruction following.
Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws).
Release Date: 06/04/2025
Version: 1.0
Model Developers: Red Hat

Quantized version of nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16.

Model Optimizations

This model was obtained by quantizing the weights of nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.

Only the weights of the linear operators within transformer blocks are quantized. Weights are quantized using an asymmetric per-group scheme with group size 128. The llm-compressor library is used for quantization.

Deployment

Use with vLLM

This model can be deployed efficiently using the vLLM backend.

Install dependencies:

uv pip install git+https://github.com/vllm-project/vllm.git
uv pip install llmcompressor

Launch the vLLM server:

vllm serve RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16 \
  --host 0.0.0.0 --port 8088 \
  --tensor-parallel-size 8 \
  --enable-expert-parallel \
  --max-model-len 262144 \
  --gpu-memory-utilization 0.90 \
  --max-num-seqs 32 \
  --max-num-batched-tokens 32768 \
  --enable-chunked-prefill \
  --enable-prefix-caching \
  --reasoning-parser nemotron_v3 \
  --mamba-ssm-cache-dtype float16 \
  --mamba-backend flashinfer \
  --enable-mamba-cache-stochastic-rounding \
  --mamba-cache-philox-rounds 5 \
  --speculative-config '{"method": "nemotron_h_mtp", "num_speculative_tokens": 5}' \
  --model-loader-extra-config '{"enable_multithread_load": true, "num_threads": 96}' \
  --trust-remote-code

Send requests:

from openai import OpenAI

openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8088/v1"

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

model = "RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16"

messages = [
    {"role": "user", "content": "Solve for x: 2x + 5 = 13"},
]

outputs = client.chat.completions.create(
    model=model,
    messages=messages,
)

generated_text = outputs.choices[0].message.content
print(generated_text)

Creation

This model was quantized using the llm-compressor library as shown below.

from llmcompressor import model_free_ptq

MODEL_ID = "nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16"
SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-W4A16-G128"

model_free_ptq(
    model_stub=MODEL_ID,
    save_directory=SAVE_DIR,
    scheme="W4A16",
    ignore=[
        "re:.*gate$",
        "lm_head",
        "model.embed_tokens",
        "re:.*mixer.conv1d.*",
        "re:.*norm_f*",
        "re:.*bias$",
        "re:.*embed_tokens$",
        "backbone.embeddings"
    ],
    max_workers=15,
    device="cuda:0",
)

Evaluation

The model was evaluated on reasoning tasks using lighteval. vLLM was used as the serving backend for all evaluations.

Install dependencies:

uv pip install git+https://github.com/vllm-project/vllm.git
uv pip install lighteval==0.13.0
uv pip install "litellm[caching]>=1.66.0"

Launch the vLLM server:

vllm serve RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16 \
  --host 0.0.0.0 --port 8088 \
  --tensor-parallel-size 8 \
  --enable-expert-parallel \
  --max-model-len 262144 \
  --gpu-memory-utilization 0.90 \
  --max-num-seqs 32 \
  --max-num-batched-tokens 32768 \
  --enable-chunked-prefill \
  --enable-prefix-caching \
  --reasoning-parser nemotron_v3 \
  --mamba-ssm-cache-dtype float16 \
  --mamba-backend flashinfer \
  --enable-mamba-cache-stochastic-rounding \
  --mamba-cache-philox-rounds 5 \
  --speculative-config '{"method": "nemotron_h_mtp", "num_speculative_tokens": 5}' \
  --model-loader-extra-config '{"enable_multithread_load": true, "num_threads": 96}' \
  --trust-remote-code

AIME 2025:

lighteval endpoint litellm \
  "model_name=hosted_vllm/RedHatAI__NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16,provider=hosted_vllm,base_url=http://127.0.0.1:8088/v1,timeout=3600,concurrent_requests=32,generation_parameters={temperature:1.0,top_p:0.95,max_new_tokens:32768}" \
  "aime25|0" \
  --output-dir results --save-details

GPQA Diamond:

lighteval endpoint litellm \
  "model_name=hosted_vllm/RedHatAI__NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16,provider=hosted_vllm,base_url=http://127.0.0.1:8088/v1,timeout=3600,concurrent_requests=32,generation_parameters={temperature:1.0,top_p:0.95,max_new_tokens:32768}" \
  "gpqa:diamond|0" \
  --output-dir results --save-details

Accuracy

Benchmark	nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16	nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4	RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-FP8-Dynamic	RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-FP8-BLOCK	RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16 (this model)
AIME 2025 (pass@1)	90.00	90.00 (100.0%)	93.33 (103.7%)	86.67 (96.3%)	86.67 (96.3%)
GPQA Diamond (pass@1)	78.79	84.85 (107.7%)	82.32 (104.5%)	81.31 (103.2%)	81.82 (103.8%)
Average	84.39	87.42 (103.6%)	87.83 (104.1%)	83.99 (99.5%)	84.24 (99.8%)

Extended Evaluation

The model was evaluated across instruct, reasoning, and coding tasks. Scores are averaged over multiple seeds (3 seeds for most benchmarks, 8 for AIME 2025). Recovery is computed as the ratio of the quantized model score to the base model score.

Category	Benchmark	nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16	RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16 (this model)	Recovery
Instruct	MMLU-CoT (5-shot)	91.09	90.95	99.85%
Instruct	GSM8K Platinum (5-shot)	98.59	98.59	100.00%
Instruct	IFEval (0-shot)	91.00	89.03	97.84%
Instruct	MATH-500	83.60	83.67	100.08%
Reasoning	AIME 2025	64.17	61.67	96.10%
Reasoning	MATH-500	85.80	85.93	100.15%
Reasoning	GSM8K Platinum (0-shot)	96.77	96.58	99.80%
Reasoning	IFEval (0-shot)	94.58	94.02	99.41%
Coding	LCB CodeGen v6	51.81	48.19	93.01%

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Safetensors

Model size

565B params

Tensor type

I64

I32

BF16

F32

Model tree for RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16

Base model

nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16

Quantized

(18)

this model