Open models like NVIDIA Nemotron give enterprises full control to customize AI for their specific business needs at sign

Enterprises have many powerful models to choose from, but the real test is whether the AI they build uniquely addresses their business needs: improving workflows, tapping into domain knowledge and exceeding standards for accuracy and trust. Competitive AI advantage increasingly comes from how organizations build with available models rather than which one they choose.

Open models like NVIDIA Nemotron are built for customization, helping enterprises build AI that is controllable, trustworthy and tailored to their specific needs. Specialized AI applications and autonomous agents are built with customized open models tuned on proprietary knowledge and evaluated against real business outcomes. This requires access to the model itself. Closed models continue to advance general intelligence capabilities but set a ceiling on what enterprises can inspect, tune and improve. Open models remove that barrier and provide complete ownership and control.

The most effective agentic AI applications are systems of models where open models work alongside leading frontier models, each fulfilling the job it does best. High-performance reasoning models can handle complex planning while smaller models execute on specialized tasks. This approach lets enterprises right-size inference costs, improve accuracy on specific tasks and maintain flexibility as workflows evolve.

Open models give enterprises something closed models cannot: full control to customize, inspect and improve AI against business needs. Public benchmarks measure general capability, but business-specific evaluation lets teams test against their own data, workflows and definition of accuracy before improving further. The cost of a wrong answer is particularly high in industries like healthcare and legal, where teams handle sensitive data and face strict accuracy requirements. Organizations in these sectors must have visibility into how a model was trained and how it performs, with the ability to improve it when necessary. With open models, teams can inspect their applications, run private evaluations against their own criteria and stand up reinforcement learning environments tuned to their own workflows without routing proprietary data through third parties.

Companies across industries are already specializing Nemotron for their domains. Customization improves accuracy, and when models are tuned for a specific domain, they run more efficiently too. The NVIDIA NeMo suite of open libraries accelerates model customization and evaluation, agent optimization and governance. Partners like Prime Intellect and Unsloth are enabling AI customization for enterprises building post-training pipelines on Nemotron, making it practical to run specialized AI at scale.

LangChain tuned its Deep Agents harness for Nemotron 3 Ultra by adjusting prompts, tools and middleware with no model retraining and achieved top agent accuracy among open models at approximately 10 times lower cost per run than leading closed alternatives. Arcee AI achieved inference costs of roughly 90 cents per million output tokens by post-training Nemotron on the NVIDIA Blackwell platform, approximately 20 times cheaper than comparable closed frontier models while ranking second on PinchBench and remaining fully open weight.

Cost savings enable broader experimentation, more deployments and faster iteration. The shift from AI adoption to AI ownership is underway. The NVIDIA Nemotron Coalition is helping turn open model development into an ecosystem effort, bringing model builders and developers together to improve Nemotron through shared data, evaluations and domain expertise. Hackathon submissions and community contributions generate reusable proof assets across industries. Builders are adding Nemotron to their AI systems, proving value and sharing what works. The foundation is entirely open.