New AI model from Nvidia available for download - LLM Nemotron 3 Nano tests on laptop

In this topic I will show how anyone can run the Nemotron 3 Nano on their own computer and then test it with all sorts of electronics and programming tasks. The Nemotron 3 Nano is a new thinking LLM model based on a Mixture-of-Experts architecture with a Mamba-2 + Transformer hybrid. The model has 30 billion parameters, but only about 3-4 billion are active during inference, significantly reducing the computational requirements.

Nemotron-3-nano-30b-a3b supports very long context (up to 1M tokens), several languages (German, French, Italian, etc.; no Polish) and released under the NVIDIA Open Model License. The model allows commercial use.

Information about the model used:
https://build.nvidia.com/nvidia/nemotron-3-nano-30b-a3b/modelcard
Online access:
https://build.nvidia.com/nvidia/nemotron-3-nano-30b-a3b


Running the model locally
Download Ollama:
https://ollama.com/
Ollama has had a normal Windows app for some time now. Just download, run, and we already have a windowed interface to talk to AI. You still need to add models within the same app, the easiest way is to download them by entering their name from the Ollama Library:
https://ollama.com/library

The model is fresh, published over a week ago.
In this case I entered: nemotron-3-nano:30b . The model occupies approximately 23 GB:

Once downloaded, you can move on to testing. It is really very simple.

Decoding the barcode of the resistors
To start with a task that I think every budding electronics engineer encounters - decoding resistors. At the same time, this task potentially requires basic processing ability, although here it could be argued that the AI model doesn't 'predict' but has only memorised every possible permutation of colours. This is difficult for me to assess.
I used our Electrode calculator for comparison:
https://www.elektroda.pl/calculators/4-band-resistor
Expected result:

Nemotron managed the task:




Inverse resistor test
Can AI determine the colours for given resistor parameters and then draw this in HTML+JS? Obviously the whole thing as a separate chat - no story.

He managed to specify the colours, but did he draw?

Here, however, we have a small glitch. The code doesn't run - JS syntax error.


Resistor for LED
Now for the second classic task, although here too I have chosen a slightly less typical voltage. Calculating the resistor for the LED:


Not only did the model handle the task, it also tried to advise the selection of the actual resistor values from the series.




Path width for current
https://www.elektroda.pl/calculators/trace-width-calculator-internal





Console stars
The next test was about programming. I tried to think of some relatively simple but also less typical tasks. I decided that the AI would have to create four rectangles from the stars in the console and display them against the letters 'B'.


It went very well - it works.

Time to modify. Will the AI understand the context? I want each rectangle to be filled with each consecutive number.

Works:




Game in HTML+JS - palette
Now some simple game playing. Another exam task for a student. I have given a somewhat precise description, but it always helps when working with AI.

Generated code:

I am surprised, but it works - I have no complaints:

I have asked for a modification - adding an opponent:

Well, sure the shortcomings are there. The opponent is across and in addition the ball sometimes gets blocked between the pallet and the wall, but still impressive.


Arduino and reading from the TC74
The TC74 is a very simple temperature sensor with an I2C interface. Reading the temperature from it comes down to reading a single byte. Does Nemotron know about this?

The generated code, on the other hand...

In my opinion, this is not a valid code. The byte read is simply the temperature, you do not need to multiply by 0.25. In addition, you only need to read one byte. I also don't like the address yet, although it depends on the TC74 version.


Classics - r letters in strawberry
This is a fairly common example, AI has trouble counting letters because it operates on tokens.
But it does wonder...


Good! How about a less typical word now? This will check if he has strawberry in the learning data.

Successful.

Logical puzzles
Some puzzles that LLMs have problems with. Tricky tasks.

Successful, although a long time for the system to think about it:

The second trick question:
This question is about counting legs - four monkeys are jumping on the bed, there are three hens on the floor. The bed has four legs. How many legs are on the floor? 3*2 + 4 = 10.

LLM replied that 6. Discussable.... he did not include the bed legs, but supposedly he should. Additionally... wrong justification as to the monkeys he gave, they don't count because they are on the bed. However, I understand that the question is indeed vaguely worded and it can be argued that indeed the bed does not count.

Information about Tasmota
Tasmota is open source for IoT devices, does Nemotron know about it?

There was a hallucination in my mind regarding the name. The rest reasonably ok:

With this JSON is debatable, unless he means cmnd API.
In general, the description pretty much matches the facts came out.

Polish language Polish language is not officially supported
Polish is not officially supported, but worth checking anyway.

Looks acceptable, only occasional language errors.


Short performance test on my old laptop
I made the presentations on an old Asus ROG series laptop. Specifications:
- Intel (R) Core(TM) i7-6700HQ CPU @ 2.60GHz
- 48 GB RAM
- GeForce GTX 1060M
The video shows what the Nemotron's live response generation looks like:


Summary
I may have taken too easy a task for this LLM, but for me the results are impressive. I didn't expect such a thing to run on my old 2018 laptop.
This model really gets to grips with the basics of electronics, logic and programming, although it lacks at least some general knowledge - it hallucinates about the TC74 communication protocol, the origin of the Tasmota name, some of its applications.
I can agree here with what Nvidia itself admits - this is a small and powerful model for agent systems, not Wikipedia.
Do you see any uses for this type of LLM model? Have you tested the Nemotron yet?