Announcing native availability of NVIDIA Nemotron 3 Nano, NVIDIA’s latest reasoning model

Nvidia has dropped its newest reasoning model, Nemotron 3 Nano, straight onto Together AI's platform, and the pitch is pretty simple: get big-model brains without the big-model bill. The model uses a hybrid Mamba-Transformer architecture layered with a sparse mixture-of-experts setup, which sounds like alphabet soup until you see the payoff. Out of a total 30 billion parameters, only about 3 billion actually activate per token. That's the trick — you get the reasoning depth of a much larger model while paying compute costs closer to a small one.

The architecture choices aren't arbitrary. Mamba layers handle long, structured sequences efficiently, which matters a lot when you're feeding a model a million tokens of context — think entire codebases, log files, or months of agent memory. Transformer layers sit alongside them to handle the harder general reasoning and instruction-following work. Nvidia says the result holds up well on coding, math, scientific reasoning, and function calling, the exact skills that agentic systems lean on when they're planning multi-step tasks or calling tools repeatedly.

And that agentic angle is really the whole point of this release. Together AI isn't just hosting the weights; it's positioning itself as the place to actually run this thing at production scale, with an OpenAI-compatible API, token streaming, and infrastructure built to survive traffic spikes without agent loops stalling out mid-reasoning. For teams running multi-agent orchestration — dozens of agents calling tools, checking each other's work, looping through plans — latency and reliability aren't nice-to-haves, they're the difference between a system that works and one that quietly falls apart under load.

Nvidia is also leaning hard into openness here: open weights, open training data, open training recipes. That's a meaningful detail for enterprises with compliance requirements or researchers who want to actually inspect what they're deploying, rather than trusting a black box. Joey Conway, who runs generative AI software at Nvidia, framed it as bringing accuracy and transparency together in a way that lets developers build specialized agents without giving up visibility into how the model was built.

Whether Nemotron 3 Nano becomes a default choice for agent builders will come down to real-world cost-per-step numbers once people start running it at volume, not just benchmark charts. But the architecture bet — sparse activation plus long context plus full openness — is a sensible one for a market that's rapidly shifting from single chatbot queries toward armies of agents grinding through tool calls all day.