How large are large language models?

When you train it to be an assistant model, it's better at compressing assistant transcripts than it is general text.

There is an eval which I have a lot of interested in and respect for https://huggingface.co/spaces/Jellyfish042/UncheatableEval called UncheatableEval, which tests how good of a language model an LLM is by applying it on a range of compression tasks.

This task is essentially impossible to 'cheat'. Compression is a benchmark you cannot game!

Lots of various sources that you can download locally to have available offline. They're even providing some pre-loaded devices in areas where there may not be reliable or any internet access.

> The English Wikipedia, as of June 26, 2025, contains over 7 million articles and 63 million pages. The text content alone is approximately 156 GB, according to Wikipedia's statistics page. When including all revisions, the total size of the database is roughly 26 terabytes (26,455 GB)

It is 64,800,000,000 bits.

I can imagine 100 bits sure. And 1,000 bits why not. 10,000 you lose me. A million? That sounds like a lot. Now 64 million would be a number I can't well imagine. And this is a thousand times 64 million!

[1]: https://en.wikipedia.org/wiki/Encyclopædia_Britannica

To me the amazing thing is that you can tell the model to do something, even follow simple instructions in plain English, like make a list or write some python code to do $x, that's the really amazing part.

the self-execution is the interactive chat interface.

wikipedia gets "trained" (compiled+compressed+lossy) into an executable you can chat with, you can pass this through another pretrained A.I. than can talk out the text or transcribe it.

I think writing compilers is now an officially a defunct skill of historical and conservation purposes more than anything else; but I don't like saying "conservation", it's a bad framing, I rather say "legacy connectivity" which is a form of continuity or backwards compatibility

One factor, is the huge redundancies pervasive in our communication.

(1) There are so many ways to say the same thing, that (2) we have to add even more words to be precise at all. Without a verbal indexing system we (3) spend many words just setting up context for what we really want to say. And finally, (4) we pervasively add a great deal of intentionally non-informational creative and novel variability, and mood inducing color, which all require even more redundancy to maintain reliable interpretation, in order to induce our minds to maintain attention.

Our minds are active resistors of plain information!

All four factors add so much redundancy, it’s probably fair to say most of our communication (by bits, characters, words, etc., may be 95%?, 98%? or more!) pure redundancy.

Another helpful compressor, is many facts are among a few “reasonably expected” alternative answers. So it takes just a little biasing information to encode the right option.

Finally, the way we reason seems to be highly common across everything that matters to us. Even though we have yet to identify and characterize this informal human logic. So once that is modeled, that itself must compress a lot of relations significantly.

Fuzzy Logic was a first approximation attempt at modeling human “logic”. But has not been very successful.

Models should eventually help us uncover that “human logic”, by analyzing how they model it. Doing so may let us create even more efficient architectures. Perhaps significantly more efficient, and even provide more direct non-gradient/data based “thinking” design.

Nevertheless, the level of compression is astounding!

We are far less complicated cognitive machines that we imagine! Scary, but inspiring too.

I personally believe that common PCs of today, maybe even high end smart phones circa 2025, will be large enough to run future super intelligence when we get it right, given internet access to look up information.

We have just begun to compress artificial minds.

Although strictly speaking they have lots of information in a small package, they are F-tier compression algorithms because the loss is bad, unpredictable, and undetectable (i.e. a human has to check it). You would almost never use a transformer in place of any other compression algorithm for typical data compression uses.

After that, make the robots explore and interact with the world by themselves, to fetch even more data.

In all seriousness, adding image and interaction data will probably be enormously useful, even for generating text.

I tied to estimate how much data this actually is:

    # annas archive stats
    papers = 105714890
    books = 52670695
    
    # word count estimates
    avrg_words_per_paper = 10000
    avrg_words_per_book = 100000
    
    words = (papers*avrg_words_per_paper + books*avrg_words_per_book )
    
    # quick text of 27 million words from a few books
    sample_words = 27809550
    sample_bytes = 158824661
    sample_bytes_comp = 28839837 # using zpaq -m5
    
    bytes_per_word = sample_bytes/sample_words
    byte_comp_ratio = sample_bytes_comp/sample_bytes
    word_comp_ratio = bytes_per_word*byte_comp_ratio
    
    print("total:", words*bytes_per_word*1e-12, "TB") # total: 30.10238345855199 TB
    print("compressed:", words*word_comp_ratio*1e-12, "TB") # compressed: 5.466077036085319 TB

That fits on three 2TB micro SD cards, which you could buy for a total of 750$ from SanDisk.

Where you getting these numbers from? Interested to see how that's calculated.

I read somewhere, but cannot find the source anymore, that all written text prior to this century was approx 50MB. (Might be misquoted as don't have source anymore).

There's no way the entire Web fits in 400$ worth of hard drives.

Did you mean to type EB?

FWIW there is a huge difference between 4.5 and 4o.

You're right the T5 stuff is very important historically but they're below 11B and I don't have much to say about them. Definitely a very interesting and important set of models though.

Thank you for spotting the error.

There are some signs it's written by possibly a non-native speaker.

In practice, models can be quantized to smaller weights for inference. Usually, the performance loss going from 16 bit weights to 8 bit weights is very minor, so a 1 billion parameter model can take 1 gigabyte. Thinking about these models in terms of 8-bit quantized weights has the added benefit of making the math really easy. A 20B model needs 20G of memory. Simple.

Of course, models can be quantized down even further, at greater cost of inference quality. Depending on what you're doing, 5-bit weights or even lower might be perfectly acceptable. There's some indication that models that have been trained on lower bit weights might perform better than larger models that have been quantized down. For example, a model that was trained using 4-bit weights might perform better than a model that was trained at 16 bits, then quantized down to 4 bits.

When running models, a lot of the performance bottleneck is memory bandwidth. This is why LLM enthusiasts are looking for GPUs with the most possible VRAM. You computer might have 128G of RAM, but your GPU's access to that memory is so constrained by bandwidth that you might as well run the model on your CPU. Running a model on the CPU can be done, it's just much slower because the computation is so parallel.

Today's higher end consumer grade GPUs have up to 24G of dedicated VRAM (an Nvidia RTX 5090 has 32G of VRAM and they're like $2k). The dedicated VRAM on a GPU has a memory bandwidth of about 1 Tb/s. Apple's M-series of ARM-based CPU's have 512 Gb/s of bandwidth, and they're one of the most popular ways of being able to run larger LLMs on consumer hardware. AMD's new "Strix Halo" CPU+GPU chips have up to 128G of unified memory, with a memory bandwidth of about 256 Gb/s.

Reddit's r/LocalLLaMA is a reasonable place to look to see what people are doing with consumer grade hardware. Of course, some of what they're doing is bonkers so don't take everything you see there as a guide.

And as far as a decade from now, who knows. Currently, the top silicon fabs of TSMC, Samsung, and Intel are all working flat-out to meet the GPU demand from hyperscalers rolling out capacity (Microsoft Azure, AWS, Google, etc). Silicon chip manufacturing has traditionally followed a boom/bust cycle. But with geopolitical tensions, global trade barriers, AI-driven advances, and whatever other black swan events, what the next few years will look like is anyone's guess.

This is exactly what I did in a previous role, fine-tuning Llama and Mistral models on a mix of human and GPT-4 data for a domain-specific task. Adding (good) synthetic data definitely increased the output quality for our tasks.

> it somehow merged Llama 4 Maverick's custom Arena chatbot version with Behemoth

I can clarify this part. I wrote 'There was a scandal as facebook decided to mislead people by gaming the lmarena benchmark site - they served one version of llama-4 there and released a different model' which is true.

But it is inside the section about the llama 4 model behemoth. So I see how that could be confusing/misleading.

I could restructure that section a little to improve it.

> Llama 405B was also trained on more than 15 trillion tokens[1],

You're talking about Llama 405B instruct, I'm talking about Llama 405B base. Of course the instruct model has been traiend on more tokens.

> why is there such a focus on token training count?

I tried to include the rough training token count for each model I wrote about - plus additional details about training data mixture if available. Training data is an important part of an LLM.