Editor’s note: This article is part of the Nemotron Labs blog series, highlighting how the latest open models, datasets, and training techniques empower enterprises to build specialized AI systems on the NVIDIA platform. Each post delves into practical applications of open stacks, showcasing their potential to deliver real value in production, from transparent research co-pilots to scalable AI agents.
Enterprises now have access to a multitude of powerful AI models. The true challenge lies in creating AI solutions that effectively meet unique business objectives, such as enhancing workflows, leveraging domain expertise, and maintaining high standards of accuracy and reliability.
The competitive edge in AI is increasingly determined by the methodologies organizations employ to build available models, rather than merely the models they select.
Among the notable open models is the NVIDIA Nemotron. Designed for customization, it enables businesses and nations to forge AI that is reliable, controllable, and tailored to specific requirements.
From Leveraging AI to Owning Intelligence
Specialized AI applications, including autonomous agents and systems, are crafted using customized open models. These agents excel in their designated tasks, as they are tuned with proprietary knowledge and validated against real-world performance metrics.
Access to the models themselves is essential. While closed models may continue pushing the boundaries of general intelligence, their limitations hinder companies’ ability to inspect, modify, and enhance their AI systems. Open models dissolve these barriers, offering total ownership and control.
The most effective agent-based AI applications are model systems, where the open model complements the dominant frontier model, enabling both to perform optimally. High-performance inference models can manage complex plans while smaller models tackle specialized tasks, empowering organizations to right-size their inference to reduce costs, enhance task-specific accuracy, and adapt readily as workflows evolve.
Customization You Can Trust
Open models provide enterprises with capabilities that closed models can’t offer. This autonomy allows organizations to customize, inspect, and optimize their AI solutions to meet specific business needs. Unlike public benchmarks that assess general performance, targeted assessments enable teams to validate against proprietary data, workflows, and metrics for success, fostering continuous improvement.
In sectors such as healthcare and law, organizations handle sensitive data under strict accuracy requirements, where missteps can be costly. These sectors demand transparency in model training, performance evaluation, and the ability to refine models as necessary.
Open models empower teams to analyze applications, conduct proprietary assessments tailored to their standards, and implement reinforcement learning in ways that align with their unique workflows — eliminating the need to funnel proprietary data through third parties.
Numerous sectors have already successfully customized Nemotron to fit their unique demands:
- Abridge has customized Nemotron to create a foundational model designed specifically for clinical conversations.
- Glean developed Waldo, an agent-search model pairing Nemotron with a large-scale closed model to achieve rapid enterprise search while reducing tokens and latency.
- Company H has advanced the Holotron 3 Nano by post-training the Nemotron 3 Nano Omni based on their own usage data, achieving 76% accuracy verified by OSWorld — a benchmark in computational tasks, competing with leading frontier models at a fraction of the cost.
- Harvey has enhanced the Nemotron 3 Ultra based on legal benchmarks to achieve frontier-class accuracy, matching leading closed models in complex legal tasks at a cost that’s at least 10x lower per execution.
- Heidi Health is delivering state-of-the-art quality outcomes in clinical documentation without the need for extensive computational resources.
- YTL AI Lab has post-trained the Malaysian Nemotron model, empowering the local developer community with customized AI to further enhance capabilities.
Fine-Tuning Your Environment and Reducing Execution Costs
Customization enhances model accuracy and efficiency when aligned with specific applications or domains.
NVIDIA NeMo, a suite of open libraries, accelerates model customization, evaluation, and agent optimization.
Partnerships like Prime Intelligence and Sloth are driving AI customization for organizations developing post-training pipelines on Nemotron, enabling the operational feasibility of specialized AI at scale.
Rung Chain has optimized the Deep Agents harness for Nemotron 3 Ultra, adjusting prompts, tools, and middleware without requiring model retraining, achieving the highest accuracy among open models at approximately 10x lower execution costs than leading closed alternatives.
These cost advantages extend to infrastructure for optimal scalability. By post-training Nemotron on the NVIDIA Blackwell platform, Arcee AI achieved an inference cost of about 90 cents per million output tokens, roughly 20 times cheaper than equivalent closed frontier models. Simultaneously, it ranked second on PinchBench with completely open weights.
Lower costs facilitate broader experimentation, increased deployment, and accelerated iteration.
An Ecosystem Built on Open Foundations
The shift from AI adoption to AI ownership is in motion. The NVIDIA Nemotron Coalition is fostering an ecosystem of open model development, uniting builders and developers to enhance Nemotron through shared data, evaluation, and domain expertise. Community-led hackathons and contributions yield reusable assets across the industry.
Builders are integrating Nemotron into their AI systems, demonstrating value, and sharing successful strategies. The foundation remains entirely open.
Discover more details NVIDIA Nemotron Open Model. Start your journey at build.nvidia.com.
Source: blogs.nvidia.com