Nemotron-3-Nano-Omni: specs, benchmarks, and how to run it on Lyceum

Integrate Nemotron-3-Nano-Omni into your agentic workflows using the standard OpenAI Python SDK. Because Lyceum Technology provides an OpenAI-compatible API, switching providers requires zero code changes to your application logic. Update the base URL and provide your Lyceum API key to route multimodal requests to our secure European infrastructure. This drop-in compatibility accelerates deployment for engineering teams migrating away from hyperscaler environments.

from openai import OpenAI

client = OpenAI(
 base_url="https://api.lyceum.technology/api/v2/external/serverless",
 api_key="<your lyceum api key>",
)
response = client.chat.completions.create(
 model="nvidia/Nemotron-3-Nano-Omni",
 messages=[{"role": "user", "content": "Hello!"}],
 max_tokens=256,
)
print(response.choices[0].message.content)

Pricing and region for Nemotron-3-Nano-Omni

This model is categorized under our Fast tier, which prioritizes cost-efficient inference for high-volume agentic tasks and perception workloads. The pricing is $0.06 per million input tokens and $0.24 per million output tokens. All inference for this endpoint is processed in the eu-north1 region, ensuring your multimodal data, whether sensitive documents, audio recordings, or video frames, remains strictly within European borders. This setup is ideal for teams building complex AI agents that require rapid perception across multiple modalities while maintaining strict data residency and compliance. Utilize per-second billing and scale-to-zero capabilities to pay strictly for the exact compute resources consumed during inference.

Unified omni-modal reasoning

Nemotron-3-Nano-Omni excels at processing multiple data types simultaneously without relying on fragmented model pipelines. Traditional agentic systems often stitch together separate vision, speech, and language models, which increases inference hops and orchestration complexity. NVIDIA designed this model to handle video, audio, image, and text inputs natively within a single perception-to-action loop. It utilizes the CRADIOv4 encoder for high-resolution vision tasks like optical character recognition (OCR) and document parsing. For audio, the Parakeet encoder processes transcription, spoken queries, and environmental sounds. Video reasoning is accelerated by 3D convolutional layers and Efficient Video Sampling (EVS), allowing the model to analyze temporal-spatial data across long clips.

High-efficiency MoE architecture

The model achieves high throughput through a hybrid Mamba-Transformer Mixture-of-Experts (MoE) architecture. While it contains 30 billion total parameters, it activates only about 3 billion parameters per token during inference. This selective activation allows Nemotron-3-Nano-Omni to operate with the speed and cost profile of a small dense model while delivering the reasoning capabilities of a much larger system. The inclusion of Mamba selective state-space layers provides efficient long-context processing, supporting a massive 256k token context window. This makes the model highly effective for analyzing multi-hour audio recordings, extended screen sessions, and dense enterprise documents without dropping critical context mid-task. According to NVIDIA's technical reports, this architecture delivers up to 9.2x higher system efficiency for video use cases compared to previous generations.

Nemotron-3-Nano-Omni benchmark results

Nemotron-3-Nano-Omni demonstrates strong performance across multimodal evaluations, particularly in document intelligence and video understanding. It competes favorably against both open-weight and proprietary models in its parameter class.

Sources: industry benchmarks, NVIDIA Technical Report (NVIDIA Technical Report).

When evaluated on MMLongBench-Doc, a rigorous test for long-context document understanding, Nemotron-3-Nano-Omni scores 61.9, outperforming the much larger Qwen3.5-397B-A17B model. This highlights the effectiveness of the CRADIOv4 vision encoder for complex enterprise documents.

In video reasoning, the model achieves a 72.2 on Video-MME, surpassing the comparable Qwen3-Omni 30B-A3B model. The integration of 3D convolutional layers and Efficient Video Sampling gives NVIDIA's model a distinct advantage in temporal-spatial tasks. For audio-visual cross-modal reasoning, it scores 74.5 on the DailyOmni benchmark. Nemotron-3-Nano-Omni activates only 3 billion parameters per token to deliver these benchmark results with significantly higher throughput and lower inference costs than dense models, making it a highly efficient choice for production multimodal pipelines.

Production configuration for Nemotron-3-Nano-Omni

Deploying Nemotron-3-Nano-Omni effectively requires understanding its context window and tier classification. The model supports a massive 256,000-token context window, which is essential for processing long-form media such as multi-hour meeting recordings, extensive PDF documents, or continuous video streams. Because it is categorized in the Lyceum Technology Fast tier, it is optimized for high-throughput, latency-sensitive applications where rapid perception is critical.

When configuring your API requests, you should carefully manage the max_tokens parameter. Because multimodal models can occasionally generate verbose descriptions of visual or audio inputs, setting a strict output limit prevents unnecessary token consumption. For example, if you are extracting specific data points from a video, capping the output at 500 tokens ensures concise responses and predictable costs.

The per-token pricing model makes this highly cost-effective for variable workloads. At $0.06 per million input tokens and $0.24 per million output tokens, processing a large batch of documents is highly economical. For instance, analyzing a dataset that consumes 5 million input tokens and generates 500,000 output tokens would cost exactly $0.30 for the input and $0.12 for the output, totaling $0.42. All requests are routed through the eu-north1 region, ensuring that your production data is processed on secure European infrastructure with zero egress fees.

Why run Nemotron-3-Nano-Omni on Lyceum

For European AI teams and enterprises handling sensitive multimodal data, data residency is a strict requirement. Running Nemotron-3-Nano-Omni on Lyceum Technology ensures that your audio recordings, video files, and proprietary documents never leave the European Union. All inference for this model occurs in our eu-north1 region, providing a clear path to GDPR and AI Act compliance. Unlike US-based API providers that route traffic through overseas data centers, Lyceum operates entirely on owned, EU-sovereign GPU infrastructure.

This structural advantage allows us to offer highly competitive pricing without the margin pressure of renting compute from hyperscalers. Utilize our serverless inference API to benefit from per-second billing and scale-to-zero functionality. You pay strictly for the tokens you consume, eliminating the need to maintain expensive, idle GPU instances for bursty multimodal workloads.

Furthermore, Lyceum Technology prioritizes open-stack transparency. We utilize optimized inference engines like vLLM and NVIDIA Dynamo rather than locking you into proprietary, black-box architectures. This ensures complete customer portability and predictable performance. If you are migrating from a hyperscaler environment, our OpenAI-compatible endpoints allow you to transition your GDPR-compliant LLM inference to Lyceum in minutes, securing your data sovereignty while significantly reducing your infrastructure costs.