Instructions to use feanors/nemotron-3-nano-omni-30b-a3b-awq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use feanors/nemotron-3-nano-omni-30b-a3b-awq with Transformers:

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

pipe = pipeline("image-text-to-text", model="feanors/nemotron-3-nano-omni-30b-a3b-awq", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("feanors/nemotron-3-nano-omni-30b-a3b-awq", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use feanors/nemotron-3-nano-omni-30b-a3b-awq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "feanors/nemotron-3-nano-omni-30b-a3b-awq"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "feanors/nemotron-3-nano-omni-30b-a3b-awq",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/feanors/nemotron-3-nano-omni-30b-a3b-awq

SGLang

How to use feanors/nemotron-3-nano-omni-30b-a3b-awq 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 "feanors/nemotron-3-nano-omni-30b-a3b-awq" \
    --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": "feanors/nemotron-3-nano-omni-30b-a3b-awq",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "feanors/nemotron-3-nano-omni-30b-a3b-awq" \
        --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": "feanors/nemotron-3-nano-omni-30b-a3b-awq",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use feanors/nemotron-3-nano-omni-30b-a3b-awq with Docker Model Runner:
```
docker model run hf.co/feanors/nemotron-3-nano-omni-30b-a3b-awq
```

Nemotron-3-Nano-Omni-30B-A3B-Reasoning - AWQ-INT4

Calibrated AWQ-INT4 W4A16 quantization of nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16, a 30B-parameter / 3B-active NemotronH hybrid model with Mamba2, Transformer attention, MoE routed experts, vision, audio, and reasoning tuning.

This artifact preserves the full multimodal stack and only packs the routed expert MLP weights to INT4. It is a compressed-tensors pack-quantized artifact, not an AutoAWQ/GEMM artifact.

Quantized by FeanorsCode as part of FeanorsCodeSL/dgx-spark-quantization.

Status

Full text eval completed on 2026-05-01 on NVIDIA DGX Spark, GB10 / SM121a, using the same vLLM image, prompts, max length, FP8 KV cache, and eval harness for AWQ, NVIDIA NVFP4, and bf16.

build	disk	GSM8K strict	GSM8K flexible	MMLU	ARC-C acc	ARC-C norm
bf16 baseline	~66 GiB	0.7900	0.9090	0.7150	0.5239	0.5631
NVIDIA NVFP4	~21 GiB	0.7589	0.8992	0.7124	0.5230	0.5401
AWQ-INT4 this build	~22 GiB	0.7983	0.8893	0.6904	0.5247	0.5589

AWQ deltas vs bf16:

metric	delta
GSM8K strict	+0.83 pp
GSM8K flexible	-1.97 pp
MMLU	-2.46 pp
ARC-C acc	+0.09 pp
ARC-C acc_norm	-0.43 pp

Model Summary

key	value
Base	`nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16`
Architecture	`NemotronH_Nano_Omni_Reasoning_V3` / `NemotronHForCausalLM`
Total params	30B
Active params	3B
Layers	52 hidden layers
Hybrid pattern	23 Mamba2 + 23 MoE MLP + 6 attention
Experts	128 routed experts, top-6 routing, 1 shared expert
Vision	RADIO / CRADIO v2-H, kept dense
Audio	Parakeet-style sound encoder, kept dense
Quantization	AWQ-INT4 W4A16, symmetric, group size 64
Format	`compressed-tensors`, `pack-quantized`
Quantized scope	routed expert `up_proj` and `down_proj` weights
Kept dense	Mamba, attention, shared experts, routers, layer 0, embeddings, `lm_head`, norms, vision, audio, projectors
Disk size	6 safetensors shards, ~21.34 GiB payload / ~22 GiB on disk

Quantization Recipe

The build uses llm-compressor AWQ calibration, then streams the packed weights into bounded safetensors shards to avoid the Transformers serializer memory spike that caused previous OOM attempts.

Reference implementation:

Recipe: runs/nemotron-3-nano-omni-30b-a3b/recipes/awq_compressed_tensors.py
Scheme doc: docs/schemes/awq-compressed-tensors.md
Full report: runs/nemotron-3-nano-omni-30b-a3b/REPORT.md

The packed artifact carries the base model's custom remote-code files and requires trust_remote_code=True.

vLLM

Tested with:

tools/serve_vllm_docker.sh \
  "$PWD/artifacts/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-AWQ-INT4" \
  --kv-cache-dtype fp8_e4m3 \
  --max-model-len 4096 \
  --gpu-memory-utilization 0.55 \
  --reasoning-parser nemotron_v3 \
  --media-io-kwargs '{"video":{"fps":2,"num_frames":256}}' \
  --video-pruning-rate 0.5 \
  --allowed-local-media-path / \
  --served-model-name nemotron-omni-awq-ct

Pinned image:

ghcr.io/spark-arena/dgx-vllm-eugr-nightly-tf5:20260428

Smoke test returned 2+2 equals 4. through /v1/chat/completions.

Multimodal smoke on downloaded public fixtures:

environment	image	audio	video
pinned `:20260428` image as-is	pass	fail	pass
pinned `:20260428` image with `av` + `soundfile` installed before server startup	pass	pass	pass

The audio failure in the bare pinned image happens during vLLM media decode before model execution (vllm[audio] extras missing). Include av and soundfile in the serving image for audio inputs.

Evaluation

Harness:

tools/run_eval_full.sh
GSM8K full set, chat-templated CoT, temperature=0, max_gen_toks=1024
MMLU full set, raw multiple-choice loglikelihood
ARC-Challenge full set, raw multiple-choice loglikelihood
max_model_len=4096
kv_cache_dtype=fp8_e4m3
reasoning_parser=nemotron_v3

Result files:

runs/nemotron-3-nano-omni-30b-a3b/results/awq_full/
runs/nemotron-3-nano-omni-30b-a3b/results/nvfp4_full/
runs/nemotron-3-nano-omni-30b-a3b/results/bf16_full/

GSM8K emitted repeated API returned null content warnings after generation for the AWQ run. The eval still completed with rc=0; samples should be inspected before relying on strict-answer behavior in a production card.

Limitations

The reported benchmark evals are text-only. Smoke tests confirm image and video requests work on the pinned image, and audio works after installing vLLM audio decode dependencies; this is not a full multimodal quality eval.
This is a compressed-tensors AWQ artifact. Consumers expecting AutoAWQ GEMM metadata will not load it as an AutoAWQ checkpoint.
The bf16 baseline required --gpu-memory-utilization 0.70 on DGX Spark. A lower 0.45 setting loaded weights but failed KV-cache allocation.

License & Usage

Model weights: governed by the NVIDIA Open Model Agreement, inherited from nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16. NVIDIA's model card states that the base model is available for commercial use.

Required NVIDIA notice: this repository includes NOTICE with: Licensed by NVIDIA Corporation under the NVIDIA Open Model License.

Quantization code and public reproduction: Apache-2.0, published by FeanorsCode at FeanorsCodeSL/dgx-spark-quantization.

Review the NVIDIA Open Model Agreement, NVIDIA Trustworthy AI terms, and any accompanying third-party component notices before production redistribution.

Citation

@misc{feanorscode_nemotron3_nano_omni_awq_int4_2026,
  title        = {Nemotron-3-Nano-Omni-30B-A3B-Reasoning AWQ-INT4},
  author       = {FeanorsCode},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/feanors/nemotron-3-nano-omni-30b-a3b-awq}},
  note         = {AWQ W4A16 compressed-tensors quantization of NVIDIA Nemotron 3 Nano Omni}
}

Credits

Model Lineage

Base model by NVIDIA: Nemotron 3 Nano Omni, a 30B / 3B-active hybrid Mamba2-Transformer MoE with CRADIO vision and Parakeet-style audio encoders.
NVIDIA's model card says the model was improved using Qwen3-VL-30B-A3B-Instruct, Qwen3.5-122B-A10B, Qwen3.5-397B-A17B, Qwen2.5-VL-72B-Instruct, and gpt-oss-120b.

Quantization

Quantized by FeanorsCode.
AWQ calibration and compressed-tensors packing used llm-compressor and compressed-tensors.
The artifact preserves Mamba, attention, shared experts, routing, embeddings, norms, lm_head, vision, audio, and projector modules at dense precision, and quantizes routed expert up_proj / down_proj weights to W4A16.

Serving & Evaluation

Served with vLLM using the community DGX Spark image ghcr.io/spark-arena/dgx-vllm-eugr-nightly-tf5:20260428.
Evaluated with lm-evaluation-harness on GSM8K, MMLU, and ARC-Challenge.
Multimodal smoke fixtures were downloaded at test time and are not redistributed in the public GitHub repo.

Downloads last month: 661

Safetensors

Model size

33B params

Tensor type

I64

I32

BF16

F32

Model tree for feanors/nemotron-3-nano-omni-30b-a3b-awq

Base model

nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16

Quantized

(61)

this model