Nvidia Nemotron 3 Family of Models
13 comments
·December 15, 2025wcallahan
btown
Would you mind sharing what hardware/card(s) you're using? And is https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B... one of the ones you've tested?
red2awn
Very interesting release:
* Hybrid MoE: 2-3x faster than pure MoE transformers
* 1M context length
* Trained on NVFP4
* Open Source! Pretraining, mid-training, SFT and RL dataset released (SFT HF link is 404...)
* Open model training recipe (coming soon)
Really appreciate Nvidia being the most open lab but they really should make sure all the links/data are available on day 0.
Also interesting that the model is trained in NVFP4 but the inference weights are FP8.
bcatanzaro
The Nano model isn’t pretrained in FP4, only Super and Ultra are. And posttraining is not in FP4, so the posttrained weights of these models are not native FP4.
pants2
If it's intelligence + speed you want, nothing comes close to GPT-OSS-120B on Cerebras or Groq.
However, this looks like it has great potential for cost-effectiveness. As of today it's free to use over API on OpenRouter, so a bit unclear what it'll cost when it's not free, but free is free!
viraptor
> nothing comes close to GPT-OSS-120B on Cerebras
That's temporary. Cerebras speeds up everything, so if Nemotron is good quality, it's just a matter of time until they add it.
credit_guy
That's unlikely. Cerebras doesn't speed up everything. Can it speed up everything? I don't know, I'm not an insider. But does it speed up everything? That is evidently not the case. Their page [1] lists only 4 production models and 2 preview models.
max002
Im upvoting, im happy to finally see open source model with commercial use from Nvidia as most of the models ive been checking from you guys couldnt be used in commercial settings. Bravo Nvidia!
kristianp
The article seem to focus on the nano model. Where are the details of the larger ones?
shikon7
> We are releasing the Nemotron 3 Nano model and technical report. Super and Ultra releases will follow in the coming months.
jtbayly
Any chance of running this nano model on my Mac?
I don’t do ‘evals’, but I do process billions of tokens every month, and I’ve found these small Nvidia models to be the best by far for their size currently.
As someone else mentioned, the GPT-OSS models are also quite good (though I haven’t found how to make them great yet, though I think they might age well like the Llama 3 models did and get better with time!).
But for a defined task, I’ve found task compliance, understanding, and tool call success rates to be some of the highest on these Nvidia models.
For example, I have a continuous job that evaluates if the data for a startup company on aVenture.vc could have overlapping/conflated two similar but unrelated companies for news articles, research details, investment rounds, etc… which is a token hungry ETL task! And I recently retested this workflow on the top 15 or so models today with <125b parameters, and the Nvidia models were among the best performing for this type of work, particularly around non-hallucination if given adequate grounding.
Also, re: cost - I run local inference on several machines that run continuously, in addition to routing through OpenRouter and the frontier providers, and was pleasantly surprised to find that if I’m a paying customer of OpenRouter otherwise, the free variant there from Nvidia is quite generous for limits, too.