Nanochat – the Best Chatgpt That $100 Can Buy

Karpathy says nanochat will become the capstone project of the course LLM101n being developed by Eureka Labs.

I guess it’s still a work in progress? Couldn’t find any other information elsewhere.

Still under development, remaining work includes tuning nanochat (current state being solid v0.1) and finalizing the in-between projects so that students can "unlock" all complexity that hides underneath: `torch.Tensor`, `torch.dist`, `.backward()`, '.compile()`, etc. And then the more ops heavy aspects.

I‘m afraid the technology will do more damage because many people will abuse it for fake news and misinformation.

This free tradition in software is I think one of the things that I love so much, but I don't see how it can continue with LLMs due to the extremely high training costs and the powerful hardware required for inference. It just seems like writing software will necessarily require paying rent to the LLM hosts to keep up. I guess it's possible that we'll figure out a way to do local inference in a way that is accessible to everyone in the way that most other modern software tools are, but the high training costs make that seem unlikely to me.

I also worry that as we rely on LLMs more and more, we will stop producing the kind of tutorials and other content aimed at beginners that makes it so easy to pick up programming the manual way.

As noble as the goal sounds, I think it's wrong.

Software is just a tool. Much like a hammer, a knife, or ammonium nitrate, it can be used for both good or bad.

I say this as someone who has spent almost 15 years writing software in my free time and publishing it as open source: building software and allowing anyone to use it does not automatically make other people's lives better.

A lot of my work has been used for bad purposes or what some people would consider bad purposes - cheating on tests, cheating in games, accessing personal information without permission, and in one case my work contributed to someone's doxxing. That's because as soon as you publish it, you lose control over it.

But at least with open source software, every person can use it to the same extent so if the majority of people are good, the result is likely to be more positive than negative.

With what is called AI today, only the largest corporations can afford to train the models which means they are controlled by people who have entirely different incentives from the general working population and many of whom have quite obvious antisocial personality traits.

At least 2 billion people live in dictatorships. AI has the potential to become a tool of mass surveillance and total oppression from which those countries will never recover because just like the models can detect a woman is pregnant before she knows it, it will detect a dissenter long before dissent turns into resistance.

I don't have high hopes for AI to be a force for good and teaching people how toy models work, as fun as it is, is not gonna change it.

While documenting a build path is nice, IMHO renting hardware nobody can afford from VC-backed cloud providers using cold hard cash to produce clones of legacy tech using toy datasets under the guise of education is propping up the AI bubble and primarily helping institutional shareholders in those AI bubble companies, particularly their hardware supplier NVidia. Personally I do not see this as helping people or humanity.

This would sit better with me if the repo included a first tier use case for local execution, non-NVidia hardware reference, etc.

I would adjust your formula to the:

number of people you help x how much you help them x number of people you harm x how much you harm them

For example - harming a little bit all content creators of the world, by stealing their work without compensation or permission. How much does that cost globally every year after year? How do we even quantify long term consequences of that? Stuff like that.

(This is a bit ranty, but due to a sincere desire for a better world, and being the recipient of personal attacks for believing a better world is achievable by a different path to others)

I feel like this point of view is an ideal not shared by one of the main branches of anti-AI sentiment.

The idea of intellectual property works against this. Rather than contributing to humanity directly, ownership of information is accumulated by individuals and then rented to humanity.

At the same time I agree that people should be able to have a livelihood that affords them the ability to create new intellectual contributions.

The service Karpathy is providing is also being provided by thousands of YouTube creators in a huge variety of topics. It's a little sad that so many must support their efforts with support their efforts with sponsorships from sources with varying degrees of ethical behaviour. Patreon is better but still not ideal. I sincerely believe this _is_ one of the best ways to contribute to society.

A recent Daily Show had Jon Stewart describe training AI as strip mining human knowledge. Training AI is regularly described as theft as if this position is a given without any counter argument possible. It is opinion masquerading as fact. This saddens me because it suggests to me that the war to control the narrative is being won by people who want to entrench a hypercapitalistic vision of ownership where not only is a particular expression of an idea ownable but also stakes a claim to own some of any ideas that come from viewing that expression.

I cannot see any way that this viewpoint would aid humanity as a whole, but instead assign benefits to a collection of individuals. The ability to trade intellectual property means that ownership inevitably gets passed to a smaller and smaller pool of individuals over time.

I think we really do need a new way to consider these issues in light of the modern world. When mentioning these thoughts to others a common refrain is that it doesn't matter because the powers that be (and their lobbyists) will prevent any fix from happening. I have never been fond of that particular fatalism, especially when it inhibits discussion of what would be better.

Then a single person whose learned those skills decide to poison all of us thanks to the skills acquired.

He is the GOAT of LLM MVPs. That is educational and useful, especially because he uses a minimal and clean style, but I don't see how it even compares with kernels, operating systems etc.

So I appreciate his work in an academic and educational sense, but large scale applications with stolen training material are still theft.

strong +1 - developers like him are heros

If it only were so easy

I recommend his ANN/LLM from scratch videos to people a lot because not only is he a clear instructor, but his code tends to be very Pythonic and just the right balance of terse but readable (not counting the Pytorch vectorization stuff, but that's not his fault, it's just complex). So I think people benefit just from watching and imitating his code style.

Thanks - we'll put that in the toptext as well

Please oh please. This would be perfect.

If I have let’s say 40gb RAM does it not work at all or just take twice as long to train?

A GPU with 80GB VRAM costs around $1-3 USD an hour on commodity clouds (i.e. the non-Big 3 bare metal providers e.g. https://getdeploying.com/reference/cloud-gpu/nvidia-h100). I think it's accessible to most middle class users in first world countries.

> Batch size of 8 would imply 20gb mem, no?

I'm running it now and I had to go down to 4 instead of 8, and that 4 is using around 22-23GB of GPU memory. Not sure if something is wrong or if batch is only scaling part of the memory requirements. (Edit: I restarted running the training script directly instead of torch run, and 8 still doesn't fit, but 4 is now using 16-17 instead.)

On my 4090 the tok/sec is 523, which is 1/2000 of the 1,000,000 tok/sec of the 8 80GB H100s. That feels too slow so maybe something is wrong. The 4090 is about 1/3 of the raw compute. I'm sure there's other losses from less batching but even if it were 1/10ths as fast, I'd expected something more like 1,000,000 / 10 / 8 so at least 10,000 tok/sec.

Yes, you can always stream data when training or doing inference on models when vram is lacking but the slow down is extremely noticeable. This is the case for CPU code too and is why optimising for bandwidth is so critical in high-performance computing. Your ability to compute is almost always substantially larger than your bandwidth. An Avx512 capable CPU with a suitable amount of cores is easily capable of doing multiple terabytes of fp64 operations per second, but is typically limited by memory bandwidth, GPUs with LLMs have just broadened this knowledge to more people.

A fun consequence of the fact that CPUs got faster at a rate quicker than memory is look up tables of pre-computed values used to be common optimisations in code, but now it is almost always quicker to re-compute them than to retrieve a pre-computed value from memory for common use-cases.

the shakespeare code tuned a little with different training data does a good job of generating Magic The Gathering commander decks

It's a useful exercise. A lot of the good ML work is first validated at small scale.

And this new example goes even further - adds instruction following and tool use SFT, as well as RLVR. Makes for a more useful baseline.

You could do that indeed, but the performance would be abysmal. For this kind of use-case, it would be a LOT better to use a small pre-trained model and either fine-tune it on your materials, or use some kind of RAG workflow (possibly both).

You could! But just like others have mentioned, the performance would be negligible. If you really wanted to see more of a performance boost by pretraining you could try to create a bigger chunk of data to train off of. This would be done by either creating synthetic data off of your material, or finding adjacent information to your material. Here's a good paper about it: <https://arxiv.org/abs/2409.07431>

You could but it would be significantly worse than fine-tuning or RAG with a pre-trained model, or using a smaller model since your dataset would be so small.

Yes, though it's possible a more-general core model, further enhanced with some other ways to bring those texts-of-interest into the working context, might perform better.

Those other ways to integrate the texts might be some form of RAG or other ideas like Apple's recent 'hierarchical memories' (https://arxiv.org/abs/2510.02375).

No.

It's a reasonable shortcut for what this project provides: training code, inference code and a ChatGPT-style web interface for chatting with the model.

Where did you find that?

not sure why you are being downvoted. That 'explanation' of Rayleigh scattering is just wrong.

That was the point. That example is meant to demonstrate that the model that trained for 4 hours can imitate a conversation but isn't actually anywhere close to being useful.

Cool. Is there a simple "howto" on running this repo with training on W&B for a programmer like me who has never done model training flows? Maybe you could share the steps you took?

The comment beside the first chart

>Our main measure of progress. Bits per byte is, per Karpathy, "a much better measure than just the typical cross-entropy loss, because it further normalizes the loss on each token by the number of bytes of that token, making the metric tokenizer-invariant".

Is so blindingly obvious, that I'm ashamed to think that I didn't think do it when trialing my own tokenizer approach on tinystories. I might go back and have a look at how well my tokenizer compared to how well I imagined it compared.

I've uploaded the model here: https://huggingface.co/sdobson/nanochat

I didn't get as good results as Karpathy (unlucky seed?)

It's fun to play with though...

User: How many legs does a dog have? Assistant: That's a great question that has been debated by dog enthusiasts for centuries. There's no one "right" answer (...)

  cd /tmp
  git clone https://huggingface.co/sdobson/nanochat
  uv run https://gist.githubusercontent.com/simonw/912623bf00d6c13cc0211508969a100a/raw/80f79c6a6f1e1b5d4485368ef3ddafa5ce853131/generate_cpu.py \
    --model-dir /tmp/nanochat \
    --prompt "Tell me about dogs."

The measures that drop exponentially like val/bpb and train/loss you should put the x-axis in log-scale. That will better show you if it's converged

I think he mentioned somewhere he used fineweb (I assume this one https://huggingface.co/datasets/HuggingFaceFW/fineweb)

karpathy/fineweb-edu-100b-shuffle: https://huggingface.co/datasets/karpathy/fineweb-edu-100b-sh...

Which is derived from HuggingFaceFW/fineweb-edu: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu

HuggingFaceTB/smol-smoltalk: https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk

And extra fine-tuning on portions of:

cais/mmlu: https://huggingface.co/datasets/cais/mmlu

openai/gsm8k: https://huggingface.co/datasets/openai/gsm8k

allenai/ai2_arc: https://huggingface.co/datasets/allenai/ai2_arc

Unwilling to deal with pytorch? You couldn't possibly hobble yourself anymore if you tried.

What are your thoughts on using JAX? I've used TensorFlow and Pytorch and I feel like I'm missing out by not having experience with JAX. But at the same time, I'm not sure what the advantages are.

If you’re not writing/modifying the model itself but only training, fine tuning, and inferencing, ONNX now supports these with basically any backend execution provider without needing to get into dependency version hell.

Once the LLM is trained you don't need the rented hardware anymore.

Nowhere does he suggest he is buying hardware.

Muon was invented by Keller Jordan (and then optimized by others) for the sake of this speedrunning competition. Even though it was invented less than a year ago, it has already been widely adopted as SOTA for model training

8xH100 is pretty wild for a single inference node.

Is this what production frontier LLMs are running inference with, or do they consume even more VRAM/compute?

At ~$8/hr, assuming a request takes 5 seconds to fulfill, you can service roughly 700ish requests. About $0.01 per request.

Is my math wrong?