This links to the nvfp4 version of the model, so they only compare it to the bf16 in this model card. If you're looking for other similar model comparisons they are in the model card of the bf16 version here [1].
While it looks "behind" the qwen equivalent model on most benchmarks, a few personal notes:
- nemotron models feel to me a bit less benchmaxxed / "stubborn". That means that they generalise a bit better, or can be tasked to solve similar but not quite identical task types to the training data (something that's hard to do w/ qwen/ds models)
- nemotron series are also open training (w/ open training recipes and some training data public)
- nvda will have an incentive to continue this kind of releases, even if other parties slowly abandon the open release of models. Whatever other incentives 3rd party labs have (i.e. meta, goog w/ gemma, the chinese labs that IPOd, etc) nvda will always want to sell hardware so their incentive to keep pushing open models is evident and will likely continue "forever".
this "DFlash" model should be used together with one of the previous two "for lower-latency speculative decoding deployments tuned for low-concurrency data center and workstation workflows".
Crazy to see how well Qwen3.6 35b-a3b is holding up, sure it is ~20% larger but it's scores are also ~20% higher with the same number of active params (excluding the IFBench).
Hopefully Qwen follows up their 3.8 launch with a new 35b-a3b
You might look at this and and be a bit disappointed by the performance against qwen and gemma models - but this is an entirely open source training pipeline, this is quite impressive and I don't think another model this performant exists with fully open source data and recipes alongside the weights.
Nvidia just throwing something "for peasants" to stay relevant. Where is competition spirit? More importantly why Nvidia is gatekeeping computing for everyday people?
I find these releases are bad taste.
Make 1TB DGX priced affordably, not some crap model for people to waste time on.
If I use this model, will I get paid by Nvidia to buy graphics cards?
I suggested to my local restaurant to pay me for eating there and pointed out it was a win-win situation because it would increase the restaurant's ARR. I was kicked out.
Of course I made a blog post afterwards about the restaurant being negative luddites and backwards.
This links to the nvfp4 version of the model, so they only compare it to the bf16 in this model card. If you're looking for other similar model comparisons they are in the model card of the bf16 version here [1].
While it looks "behind" the qwen equivalent model on most benchmarks, a few personal notes:
- nemotron models feel to me a bit less benchmaxxed / "stubborn". That means that they generalise a bit better, or can be tasked to solve similar but not quite identical task types to the training data (something that's hard to do w/ qwen/ds models)
- nemotron series are also open training (w/ open training recipes and some training data public)
- nvda will have an incentive to continue this kind of releases, even if other parties slowly abandon the open release of models. Whatever other incentives 3rd party labs have (i.e. meta, goog w/ gemma, the chinese labs that IPOd, etc) nvda will always want to sell hardware so their incentive to keep pushing open models is evident and will likely continue "forever".
[1] - https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-...
Yes, there are 5 Nemotron-3.5 models:
https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-...
to be used for further training/fine-tuning.
https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-...
main model.
https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-...
quantized version of the previous.
https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-...
this "DFlash" model should be used together with one of the previous two "for lower-latency speculative decoding deployments tuned for low-concurrency data center and workstation workflows".
https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-...
like DFlash, the previous model above, but optimized for DGX Spark.
Thanks for pointing that out!
I actually copied the link from NVIDIA's Technical Blog post:
- https://developer.nvidia.com/blog/nvidia-nemotron-3-5-lightn...
You can also try the model via a free API endpoint from Openrouter, would be interesting to see if it's the BF16 or NVFP4 version:
- https://openrouter.ai/nvidia/nemotron-3.5-lightning:free
> nvda will have an incentive to continue this kind of releases, even if other parties slowly abandon the open release of models
This! It's literally in their best interest for open-weights models to succeed
Crazy to see how well Qwen3.6 35b-a3b is holding up, sure it is ~20% larger but it's scores are also ~20% higher with the same number of active params (excluding the IFBench).
Hopefully Qwen follows up their 3.8 launch with a new 35b-a3b
What’s expected in an updated 3.6 release?
That SWEBench drop going from bf16 to fp4 is massive
Developing on the Mamba 2 architecture is a really interesting point to note. It seems to be catching up to “regular” transformer architectures.
You might look at this and and be a bit disappointed by the performance against qwen and gemma models - but this is an entirely open source training pipeline, this is quite impressive and I don't think another model this performant exists with fully open source data and recipes alongside the weights.
Nice cadence of releases by the Nemotron team :)
Nvidia just throwing something "for peasants" to stay relevant. Where is competition spirit? More importantly why Nvidia is gatekeeping computing for everyday people?
I find these releases are bad taste.
Make 1TB DGX priced affordably, not some crap model for people to waste time on.
First convince the DRAM suppliers to drop their prices for you and then maybe nvidia will drop the DGX price for you too.
they are selling the shovels not the gold
If I use this model, will I get paid by Nvidia to buy graphics cards?
I suggested to my local restaurant to pay me for eating there and pointed out it was a win-win situation because it would increase the restaurant's ARR. I was kicked out.
Of course I made a blog post afterwards about the restaurant being negative luddites and backwards.