NVIDIA releases Nemotron 3 Ultra, a 550B-parameter agentic model
NVIDIA has released Nemotron Ultra, a 550 billion parameter mixture-of-experts model with 55 billion active parameters, designed specifically for agentic use cases including tool use, coding, and long-horizon multi-step tasks.
Score breakdown
Nemotron 3 Ultra is notable as a large open-weight model that NVIDIA explicitly trained for agentic benchmarks and released alongside its training recipes and datasets, giving organizations a documented path to fine-tune it for enterprise-scale deployments.
- 01Nemotron 3 Ultra has 550 billion total parameters with 55 billion active, in a mixture-of-experts (MoE) architecture.
- 02The model is designed for agentic use cases: tool calling, coding, and long-horizon multi-step tasks — not general chat.
- 03It is the third model in the Nemotron 3 family, following Nemotron 3 Nano and Nemotron 3 Super.
NVIDIA has released Nemotron 3 Ultra, the latest and most capable entry in its Nemotron 3 model family, which also includes the Nano (small, high-efficiency) and Super (multi-agent focused) variants. Nemotron 3 Ultra is a 550 billion parameter mixture-of-experts model with 55 billion parameters active at inference time. It is explicitly designed for agentic workflows — writing, tool use, coding, and long-horizon multi-step tasks — positioning it as NVIDIA's answer to large open-weight models from Chinese labs such as Kimi K2 and others, as well as frontier proprietary models like Anthropic's Opus and recent GPT and Gemini Pro series models. Sam Witteveen's video, sponsored by NVIDIA, walks through the model's specs, its multi-teacher distillation approach, post-training for agent harnesses, reinforcement learning environments, and benchmark performance including results on Pinchbench, followed by a live demo using the NVIDIA Cloud API covering reasoning modes and tool calling.\n\nA recurring theme in the video is NVIDIA's transparency around how the model was built: the release includes published datasets and training recipes, which Witteveen argues makes it particularly valuable for large organizations that want to fine-tune the model for specific enterprise tasks. He notes a growing trend of major companies — citing LinkedIn and Pinterest as examples discussed on a separate podcast — taking open-weight models and customizing them at scale to replace proprietary model providers. Because Nemotron 3 Ultra is likely too large to run locally, Witteveen frames it as a model suited for on-premises deployment and fine-tuning by larger organizations rather than individual developers.
Key facts
- 01Nemotron 3 Ultra has 550 billion total parameters with 55 billion active, in a mixture-of-experts (MoE) architecture.
- 02The model is designed for agentic use cases: tool calling, coding, and long-horizon multi-step tasks — not general chat.
- 03It is the third model in the Nemotron 3 family, following Nemotron 3 Nano and Nemotron 3 Super.
- 04NVIDIA published datasets and training recipes alongside the model release.
- 05The video covers multi-teacher distillation, post-training for agent harnesses, and RL environments used in training.
- 06Witteveen describes the model as NVIDIA's attempt to compete with frontier models like Anthropic Opus and recent GPT and Gemini Pro series.
- 07The video includes a live demo via the NVIDIA Cloud API showing reasoning modes and tool calling.
Topics
Summary and scoring are generated automatically from the original article. We always link back to the publisher and never republish images or paywalled content. Last processed Jun 9, 2026 · 17:05 UTC. How this works →