Instructions to use nvidia/Nemotron-3.5-Parse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/Nemotron-3.5-Parse with Transformers:

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

pipe = pipeline("image-text-to-text", model="nvidia/Nemotron-3.5-Parse", 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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("nvidia/Nemotron-3.5-Parse", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nvidia/Nemotron-3.5-Parse with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/Nemotron-3.5-Parse"
# 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-3.5-Parse",
		"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/nvidia/Nemotron-3.5-Parse

SGLang

How to use nvidia/Nemotron-3.5-Parse 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-3.5-Parse" \
    --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-3.5-Parse",
		"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 "nvidia/Nemotron-3.5-Parse" \
        --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-3.5-Parse",
		"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 nvidia/Nemotron-3.5-Parse with Docker Model Runner:
```
docker model run hf.co/nvidia/Nemotron-3.5-Parse
```

Nemotron-3.5-Parse / reference /parsebench /comparisons /README.md

emelryan

sosahi

ronay-nv

Nemotron 3.5 Parse release

dca5b2d 16 days ago

preview code

Raw History Blame Contribute Delete

3.9 kB

ParseBench comparison sources

The main card shows 10 selected comparison models and single-, double- and triple-pass Nemotron 3.5 Parse profiles. It uses published overall scores, without replacing them with reproduced scores. The CSV contains the same 13 rows; results.json adds provenance and retains the earlier reproduction sweep for reference.

Scores

The selected comparison scores were checked against the current upstream leaderboard on October 17, 2026. Its revision remains 9af4102e26b51f234ab5f037e19fec05c0696ec6; the published scores are unchanged. Displayed speeds use the completed matched prepared-page runs below.

Comparison-model scores come from the pinned ParseBench leaderboard or, for Nemotron Parse 2.0, its released model card. Published protocols and scorer versions can differ from our reproduction runs. Category-level differences and reproduced results remain in the JSON; they do not replace the published headline scores.

The Nemotron 3.5 rows use scored outputs from the same measured runs as their speed receipts:

Single-pass: 72.74, from baseline/single in scheduler benchmark receipt.
Double-pass: 74.92 and triple-pass: 75.20, from fresh_full_runs in accuracy restoration receipt.

All three cover 2,078 inputs and use the released weights with SHA-256 81dd7609c589f3694ce75520c25af5010953231358de6aec4d03e8c6aac787e9. The scored prediction digests match the measured-run artifacts. These results use the scorer identified in their receipts. The existing reference-pipeline triple-pass headline and its category metrics remain unchanged; its 75.20 rounds to the same value as the newer general helper's triple-pass result.

Throughput

Every displayed speed uses pages/s on the same 2,078 prepared ParseBench page images, with one H100 per model. The ten comparison models were rerun with their native model stages enabled, including detection, recognition, and dependent region or chart rereads where applicable.

The timer spans the first model call through the final response, including server queueing, response transfer, and gaps for dependent crop preparation and dispatch. Concurrent call durations are not added. Initial page preparation, server startup, untimed warmup, final document rendering and file writes are excluded. Paddle includes its local learned layout detector and VLM calls; MinerU is timed before final Markdown rendering.

Matched inference timing records all ten comparison models, receipt/prediction digests, exact input-image identity, coverage, and current weight checks. Nemotron inference timing records the single/double/triple profiles at 15.42 / 11.65 / 10.62 pages/s. Every comparison run has 2,078 unique outputs; successful empty responses are preserved as empty predictions rather than omitted or regenerated.

Rates count processed pages, including token-limited responses. The Nemotron receipt also reports untruncated-page counts and rates. Some native SDKs do not expose per-page finish reasons. Published accuracy scores remain separate from these speed measurements; the Nemotron rows' scored prediction digests are checked against their timing runs.

Historical full-document timings remain explicitly named in the JSON's models records. The displayed card, CSV and published_comparison array use the matched prepared-page timings. These are measured serving profiles, not a claim of maximum hardware throughput.