NVIDIA has used its Nemotron Labs blog series to highlight a growing roster of enterprise partners building specialized AI agents on its open Nemotron model family, with documented inference cost reductions of up to 20 times compared with leading closed alternatives, underscoring a widening divide between open and proprietary AI economics.
Open weights as competitive strategy
According to NVIDIA, competitive AI advantage increasingly comes from how organizations build with available models, more than which one they choose.
Open models like NVIDIA Nemotron are built for customization, helping enterprises and nations build AI that is controllable, trustworthy and tailored to their needs.
NVIDIA describes Nemotron as a family of open models providing not only weights but also training data and recipes, with models and training data published openly on Hugging Face. The family is optimized to complete agentic tasks faster with the highest throughput, and models achieve leading accuracy for long-running agentic systems through advanced reinforcement learning techniques.
This approach mirrors the broader push across the industry for transparent, auditable AI, a trend also visible in how NVIDIA expands its cloud infrastructure to support flexible deployment at scale.
Real-world partner results
Abridge is customizing Nemotron to build the first foundation model purpose-built for clinical conversations. Glean built Waldo, an agentic search model that pairs Nemotron with larger closed models to deliver enterprise search at significantly lower latency and with fewer tokens.
Harvey post-trained Nemotron 3 Ultra on its legal benchmark and reached frontier-class accuracy, matching leading closed models on complex legal tasks at at least 10 times lower cost per run.
H Company built Holotron 3 Nano by post-training Nemotron 3 Nano Omni on proprietary computer-use data, achieving higher than 76 percent accuracy on OSWorld-Verified, a benchmark on computer tasks, and matching other leading frontier models at a fraction of the cost.
YTL AI Labs post-trained a Nemotron model for the Malaysian language, putting locally customized AI in the hands of Malaysia’s developer community to further its AI capabilities.
That case illustrates the sovereignty angle NVIDIA is promoting: governments and regional players can build and own models trained on their own data rather than routing it through a third-party provider.
Infrastructure cost advantage
By post-training Nemotron on the NVIDIA Blackwell platform, Arcee AI achieved inference costs of roughly 90 cents per million output tokens, approximately 20 times cheaper than comparable closed frontier models, while ranking second on PinchBench and remaining fully open weight.
LangChain tuned its Deep Agents harness for Nemotron 3 Ultra, 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.
The NVIDIA NeMo suite of open libraries accelerates model customization and evaluation, in addition to agent optimization and governance. Partners including Prime Intellect and Unsloth are already enabling AI customization for enterprises building post-training pipelines on Nemotron, making it practical to run specialized AI at scale.
The cost dynamic is drawing attention from hardware-adjacent sectors as well. Developers building next-generation AI-powered hardware are evaluating open model stacks like Nemotron as a way to reduce total inference cost and avoid lock-in to any single frontier provider.
Ecosystem and what comes next
NVIDIA has also used the Nemotron name more broadly for associated datasets, training recipes and developer tools; in March 2026, the company formed the Nemotron Coalition, a group of AI labs collaborating on future open models.
NVIDIA describes the shift underway as moving from AI adoption to AI ownership, with the Nemotron Coalition designed to turn open model development into an ecosystem effort by bringing model builders and developers together to improve Nemotron through shared data, evaluations and domain expertise.
Open models give enterprises something closed models cannot, according to NVIDIA: full control to customize, inspect and improve AI against business needs, with business-specific evaluation letting teams test against their own data, workflows and definition of accuracy before improving from there.
The Nemotron Coalition’s inaugural members, Black Forest Labs, Cursor, LangChain, Mistral AI, Perplexity, Reflection AI, Sarvam and Thinking Machines Lab, will collaborate on the development of an open model trained on NVIDIA DGX Cloud, with the first model built by the coalition set to underpin the upcoming NVIDIA Nemotron 4 family of open models. H Company, NAVER Cloud, Nous Research and Prime Intellect have since joined the coalition as additional members. With Nemotron 3 Ultra now available and the Nemotron Coalition working toward the Nemotron 4 family, the pace of open-model development at NVIDIA shows no signs of slowing.
