How large are large language models?

It's extremely interesting how powerful a language model is at compression.

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!

A neat project you (and others) might want to check out: https://kiwix.org/

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.

8.1 GB is a lot!

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!

For reference (according to Google):

> 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)

the study of language models from an information theory/compression POV is a small field but increasingly impt for efficiency/scaling - we did a discussion about this today https://www.youtube.com/watch?v=SWIKyLSUBIc&t=2269s

The Encyclopædia Britannica has about 40,000,000 words [1] or about 0.25 GB if you assume 6 bytes per word. It’s impressive but not outlandish that an 8.1 GB file could encode a large swath of human information.

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

Back in the '90s, we joked about putting “the internet” on a floppy disk. It’s kind of possible now.

Intelligence is compression some say

Simply add images and video, and these estimates start to sound like the "640 KB should be enough for everyone".

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.

Just a nitpick, but please don’t misuse big O notation like that. Any fixed storage amount is O(100TB).

> All digitized books ever written/encoded compress to a few TB.

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.

Maybe you're thinking of Library of Congress when you say ~50TB? Internet is definitely larger..

> All digitized books ever written/encoded compress to a few TB. The public web is ~50TB. I think a usable zip of all english electronic text publicly available would be on O(100TB).

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).

This is kind of related to the jack morris post https://blog.jxmo.io/p/there-are-no-new-ideas-in-ai-only he discusses how the big leaps in LLMs have mostly come - not so much from new training methods or arch. changes as such - but the ability of new archs. to ingest more data.

> 50TB

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

>The public web is ~50TB

Did you mean to type EB?

The Gemma models are too small to be included in this list.

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.

I think that one thing that this chart makes visually very clear is the point I about GPT-3 being such a huge leap, and there being a long gap before anybody was able to match it.

This is really awesome. Thank you for creating that. I included a screenshot and link to the chart with credit to you in a comment to my post.

I have corrected that. It was supposed to say "None of this document was written by AI."

Thank you for spotting the error.

I thought this was an accidental double negative by the author -- trying to declare they wrote it themselves.

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

I don’t think the author knows that double negatives in English in a sentence like this cancel, not reinforce, each other.

You are absolutely right! The AI slop is getting out of control.

Agreed, especially when in this context of training a smaller model on a larger model’s outputs. Distillation is generally accepted as an effective technique.

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.

As a rule of thumb, each billion parameters requires about 4GB of VRAM in FP16 (2 bytes per parameter), so a 7B model needs ~28GB, 70B needs ~280GB, while the 405B models need ~1.6TB of VRAM - though quantization can reduce this by 2-4x (4-bit models use only ~0.5GB per billion parameters).

At 1byte/param that's 1.6GB (f8), at 2 bytes (f16) that's 2.3GB -- but there's other space costs beyond loading the parameters for the GPU. So a rule of thumb is ~4x parameter count. So round up, 2B -> 2*4 = 8GB VRAM

Most of these models have been trained using 16-bit weights. So a 1 billion parameter model takes up 2 gigabytes.

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.

Re 1 - classical compression is also extremely effective if both sender and receiver have access to the same huge dictionary.