NVIDIA Debuts OpenReasoning-Nemotron: Open-Source Reasoning LLMs in Four Sizes

NVIDIA has expanded its presence in open-source AI with the release of OpenReasoning-Nemotron, a new family of large language models specifically tuned for reasoning-intensive tasks. The models come in four parameter sizes—1.5B, 7B, 14B, and 32B—providing flexibility for a wide range of hardware setups. Distilled from DeepSeek R1 0528, these models aim to deliver state-of-the-art reasoning capabilities across math, science, and code benchmarks.

The OpenReasoning-Nemotron series is positioned as an open alternative to proprietary reasoning LLMs, targeting developers, researchers, and enterprises that require transparent, reproducible AI solutions. NVIDIA’s official blog highlights the models' performance on challenging reasoning datasets, outperforming several existing open-source models of comparable size. The inclusion of reinforcement learning recipes allows further fine-tuning for domain-specific reasoning tasks, supporting both academic research and production-grade deployment.

An important aspect of this release is local deployability. By releasing all four models on Hugging Face with straightforward inference pipelines, NVIDIA lowers the barrier to entry for organizations and developers concerned about privacy, latency, or cost issues associated with API-based models. The models also incorporate the GenSelect methodology, a specialized decoding strategy designed to optimize reasoning accuracy during inference.

This launch signifies NVIDIA’s growing commitment to open AI development while directly competing with other open reasoning LLM projects like DeepSeek, OpenHermes, and Meta’s CodeLlama. For AI practitioners, OpenReasoning-Nemotron offers a practical toolset for building high-performance, reasoning-driven applications without the constraints of closed APIs. For enterprises, it provides a flexible option for integrating powerful reasoning models into workflows while maintaining data control.

NVIDIA’s move could also stimulate further innovation in reasoning optimization techniques. With public access to both inference code and fine-tuning recipes, academic institutions and startups alike are positioned to experiment and iterate on reasoning LLMs more freely. As reasoning ability becomes a key differentiator in applied AI, OpenReasoning-Nemotron could play a central role in shaping future LLM applications.

NVIDIA’s playbook here is pretty transparent. They are not just feeding the open-source AI crowd scraps while keeping their best tech behind closed doors. Instead, they are seeding an entire reasoning ecosystem that conveniently aligns with their longer-term enterprise ambitions. Releasing models from 1.5B up to 32B is not charity—it’s a calculated move to capture developer mindshare and solidify their position as the backbone of AI infrastructure, all while letting the open-source crowd do the field testing.

And the timing? Very deliberate. OpenAI is busy converting ChatGPT into a gated service, Google is chasing enterprise relevance with Gemini, and Meta has been too distracted hiring away competitor talent to fix Llama’s disappointing rollout. NVIDIA steps in with open reasoning models and clean Hugging Face integrations just as open-source trust is wobbling. The result is not just another set of models, but a well-timed statement of influence in a market craving competence over corporate chaos.