Aug 11, 2026
Latest PostCompression is prediction
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Quantization from the ground up
A complete guide to what quantization is, how it works, and how it's used to compress large language models
Aug 11, 2026
Latest PostA complete guide to what quantization is, how it works, and how it's used to compress large language models
I was reading about compression recently when I stumbled upon something crazy: that compressors and LLMs are, at their core, trying to solve the exact same problem.
In this post, I’m going to walk us through the basics of compression to understand its deep relationship with language modeling. It’s probably going to blow your mind.
There are many ways of shrinking data. Take minification, for example: it works by stripping code down to the bare minimum that machines need to parse. Human-readable variables are reduced to single letters; whitespace and comments are removed.
Click “Minify” to see it in action:
The resulting file is considerably smaller, and yet you’d almost never hear minification mentioned in the field of data compression. Why is that?
Minification is fairly straightforward: it just tosses out any syntax that’s not required by machines. But “true” compression relies on redundancy to condense data.
Consider the string of nine A’s, four B’s, two C’s, one D, three A’s, then nine D’s: there’s a lot of redundancy here. We could encode this as a shorter string by noting the total run of each character in order:
Original string: 9 A's, 4 B's, 2 C's, 1 D, 3 A's, 9 D's — 28 characters, 224 bits.
Replacing each run with its character and how many times it repeats gives A9B4C2D1A3D9 — 12 characters, 96 bits, 57 percent smaller.
Using standard 8-bit ASCII encoding, our original string requires 224 bits, whereas our compressed string (“A9B4C2D1A3D9”) needs only 96. Not bad!
The above technique is just one compression method (it’s called run-length encoding), but we can do much better. Actual compressors like gzip, Brotli, etc, rely on several methods to shrink data. Let’s take a look.
There are roughly three “organs” of modern compression tools: transforms, models, and entropy coders. I’m talking about these terms as if they were clear and distinct things, but the lines can get a little blurry, and they are rarely used in isolation.
100101110
Transforms are the preprocessing steps that make our data easier to compress. The method we saw earlier (run-length encoding) is an example of a transform, but it’s worth noting that transforms don’t always shrink the data. Sometimes they can be used to create more redundancy, and the more redundancy, the more we can compress later on. We aren’t going to focus on transforms in this article, but they’re still an important part of any compression tool.
Models describe the shape of our data based on the frequencies of each symbol (whatever unit we’re using to look for redundancies: letters, numbers, tokens, or even binary code). For now, you can think of a model as a table that maps each symbol to its probability, but as we’ll see later on, they can get a lot more sophisticated.
Here’s an example based on our earlier string:
Original string: 9 A's, 4 B's, 2 C's, 1 D, 3 A's, 9 D's — 28 characters.
Counted by symbol: 12 A's, 10 D's, 4 B's, 2 C's.
Entropy coders are almost always the final step in any compression algorithm and are what produce the final compressed artifact: a raw bitstream, which is just a bare sequence of bits with none of the structure a file format would wrap around it.
I want to focus on the last two steps, because this is important. Our data model hands the entropy coder a set of probabilities to encode your data as efficiently as possible. Probabilities go in, compressed bitstream comes out:
| Symbol | Probability |
|---|---|
| A | 0.429 |
| D | 0.357 |
| B | 0.143 |
| C | 0.071 |
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Now, let’s be honest: this is all still a bit hand-wavy. What does an entropy coder even DO with all these probabilities? How does that help it do the squishing?
Every entropy coder is a unique snowflake, and the way they use probabilities to compress your data differs wildly. To keep things simple, we’re going to focus on just one for now: arithmetic coding. I’m choosing it because it best illustrates how better probabilities make for better compression.
What if I told you that you could represent an entire dataset with a single number? Does this sound crazy? I thought so too, but that’s exactly what arithmetic coding promises.
Let’s say we want to compress the string A B A B A A C. We can find the probabilities of each symbol (character) by dividing the total count by the total length of the string, which is 7:
Original string: 1 A, 1 B, 1 A, 1 B, 2 A's, 1 C — 7 characters.
Counted by symbol: 4 A's, 2 B's, 1 C.
We can represent these probabilities on a range from 0-1.
The range from 0 to 1, divided into one section per symbol, each as wide as that symbol’s probability and ordered widest first: A covers 0 to 0.571, B covers 0.571 to 0.857, C covers 0.857 to 1.
With this setup, we’re ready to do the actual compressing.
For each symbol in our string, starting with “A”, we shrink our range to fit within that symbol’s section. Importantly, we’re still dividing that new range with the same probabilities, but they now have new, smaller ranges.
Click the arrows to encode each symbol and see how the range shrinks over time:
Interactive, step-by-step illustration of arithmetic coding for the string A B A B A A C, where every symbol shares one probability distribution. It starts at the full range from 0 to 1 with nothing encoded. Each step highlights the slice the next symbol encodes into, then reveals the range that slice becomes. The last step zooms in on the final range.
Once we run out of symbols, we end up with a teeny weeny baby range: [0.38730, 0.38855).
The final number that will represent our entire data can be any number in this range, and ideally, it should be the number that requires the fewest bits possible. You can calculate this with a bit of math, but because I’m nice I’ll just give you the answer: 0.3876953125. So let’s compare: Our original string, A B A B A A C, in its raw 8-bit ASCII code requires 56 bits in total, whereas our final number requires only 10.
So, we have our magical number, but how do we use this to decode our original message? Buckle up, this is going to seem like a magic trick.
In addition to our magic number, our decompressor also receives the same probabilities we used to compress so it can rebuild that starting range of [0, 1). To decode our original message, it finds which section our magic number falls into and records that symbol. Then it shrinks the range to fit within that section, and repeats the whole process.
Give it a try:
Interactive, step-by-step illustration of arithmetic decoding of the number 0.3876953125. Symbol probabilities split the range from 0 to 1 into slices: A 57 percent, B 29 percent, C 14 percent. Each step zooms in on the current range, shows where the number falls inside it — that slice is the decoded symbol — and then reveals the range that slice becomes. The last step decodes the last symbol, recovering the whole string.
Pretty neat, huh?
We’ve now seen how an entropy coder can compress our data using a set of probabilities. As cool as arithmetic coding is (it’s not just me, right?), much of the heavy-lifting comes from the model. Remember: compression loves redundancy. Given this, what do you think would happen if our symbols had more repetition?
Here’s a new string where the letter A dominates, with a probability of 0.833.
Original string: 10 A's, 1 B, 1 C — 12 characters.
Counted by symbol: 10 A's, 1 B, 1 C.
It turns out, this skewed probability distribution makes a big difference. Let’s see how it stacks up against our old string when we apply arithmetic coding:
Our first string managed to compress to an average of 1.38 bits/symbol, whereas our longer string compressed to 0.82 bits/symbol. When your data is more skewed (i.e. the higher the probabilities of some of your symbols), the better the compression ratio.
This avg bits/symbol is a very important number. It’s called entropy, and it is the bedrock of compression.
Consider the following sentence:
“Yesterday I saw an animal when I was walking downtown. It was a _____.”
How many guesses do you think it would take you to fill in the blank? If it was a common animal like bird, you might get it on the first try. But what if the answer was bear? That would probably take quite a few guesses.
Let’s say these are the possible answers, along with their probabilities written as fractions:
Knowing the probabilities, we can actually calculate how many guesses it would take to guess correctly, on average, per animal.
Now, notice that each animal is half as likely as the one before, with the exception of fox and bear (these are probabilities, so our numbers need to add up to 1). If we were to guess each animal in order, from most probable to least, we’d have a 50/50 chance of being right each time. As such, we can determine the number of guesses it would take to guess a given animal (on average) using a yes/no decision tree. We start with the most likely animal at the top, and work our way down:
A yes/no decision tree over the animals. Is it a bird? If yes, bird, 1 guess. If no, is it a squirrel? If yes, squirrel, 2 guesses. If no, is it a cat? If yes, cat, 3 guesses. If no, is it a fox? If yes, fox, 4 guesses. If no, bear, 4 guesses.
Let’s get back to compression. Symbols with higher probabilities help us compress better, and we see the same pattern in our decision tree: the more probable animals require fewer guesses. If we treat the animals as symbols and swap the yes’s and no’s for 1’s and 0’s, the number of guesses becomes exactly the number of bits needed to represent each one. If we record the 1’s and 0’s we take to reach each animal you’ll see that the more common animals get shorter “codewords” (unique sequences of bits), and rarer animals get longer ones.
A yes/no tree over the animals. Each yes branch is labeled 1 and each no branch is labeled 0, so an animal’s code word is the branch labels that reach it. Is it a bird? If yes, bird, code word 1. If no, is it a squirrel? If yes, squirrel, code word 01. If no, is it a cat? If yes, cat, code word 001. If no, is it a fox? If yes, fox, code word 0001. If no, bear, code word 0000.
Assigning codewords to symbols like this is actually another type of entropy coder called Huffman coding, which is used in popular tools like gzip and Brotli. Instead of encoding our data into a single number, like with arithmetic coding, the Huffman method creates codewords to represent each symbol.
But there’s a problem: what happens when our probabilities aren’t neatly divided in half? If cat had a probability of 0.3973, then the likelihood of the answer being a cat or not a cat isn’t 50/50 anymore. Every path down the tree is a whole number of “guesses”, so we’re forced to round, and rounding means paying for bits we don’t need. How can we tell the absolute fewest number of bits required to represent a given symbol?
Turns out we can calculate this with a little bit of math:
If we plug in our animal probabilities, you’ll see we get the same number of bits as guesses from our decision tree:
If we get the average negative log base 2 of probability of all our symbols, that tells us our entropy.
The most important thing to understand about entropy is that it’s the floor. This is the smallest number of bits per symbol we can achieve for a given set of data. It ain’t getting any more squished.
But wait, if there’s really a limit to how much you can compress data, why isn’t there just one mega God-compressor that we use on everything? Well, that’s because entropy is specific to a set of probabilities. If we can make our probability distribution more skewed, we can compress things more.
But how do we do that?
Up until now, we’ve been working with a very simple type of model that only cares about a symbol’s frequency. count / total_symbols
= its probability.
But context can greatly affect a symbol’s probability. For example, in the entire English language, the letter U has a probability of ~0.028. However, when preceded by a Q, this shoots up to ~0.999.
Wowza.
On top of that, higher probabilities compress into fewer bits. We saw this before in the arithmetic coding section, but now we can prove it with math:
Using a single context to determine the probability of a symbol is called an order-1 model. It answers the question, “Given (some context), what is the probability of (symbol)?” With order-1, you factor in the previous symbol as your context, but you could expand this to order-2, order-3, order-4, and so on, which look at the previous N symbols.
But how do we feed this into an entropy coder? Previously our model was just a table of probabilities per symbol, but with context, we suddenly have a whole set of tables, one for each preceding symbol. So what do we do?
Let’s see what happens when we apply arithmetic coding to the string “TO BE OR NOT TO BE” using an order-1 model. Notice that with each symbol we encode, our new ranges contain a different set of probabilities.
Give it a try:
Interactive, step-by-step illustration of arithmetic coding for the string T O space B E space O R space N O T space T O space B E, where each symbol's probability is conditioned on the previous symbol. It starts at the full range from 0 to 1 with nothing encoded. Each step highlights the slice the next symbol encodes into, then reveals the range that slice becomes. The last step zooms in on the final range.
Ok, but how much does using order-N models actually impact compression?
Take a look:
Wow! Using an order-1 model cut our compressed output by more than half! Clearly, adding context gives us stronger probabilities. In other words, it helps us predict what symbol comes next.
Do you know what else is really good at prediction?
To say that there’s an overlap between LLMs and compression would be a huge understatement. In fact, in 2023, Google DeepMind released a paper arguing that language modeling and compression are two views of the same thing.
This might seem like an odd claim. After all, when you think of using LLMs, you probably think of typing a prompt into an AI chatbot and it responding with an answer. How is that compression?
Well, it’s not, but stick with me.
You might have heard LLMs described as “fancy autocomplete”, and this is essentially true. When you submit a prompt to an LLM, that becomes the context the model uses to return a set of probabilities for the next possible words. It then chooses one of those options and appends it to the context. Rinse and repeat. That’s how LLMs generate text.
Give it a whirl:
While we’re here, let’s get some terminology straight. With LLMs, what it returns aren’t technically “words” but tokens: numbers that represent words or parts of words. Tokens are the vocabulary an LLM uses to parse context and generate responses.
Now consider this: while entropy coders are what produce the final raw bitstream, there’s nothing in them that you can tweak to get better results. They are fixed, deterministic, and lossless. If you want better compression, you need to tweak the model so we get higher probabilities per symbol. In other words, we need a better predictor. And when it comes to prediction, LLMs are basically as good as it gets:
| Symbol (token) | Probability |
|---|---|
| falls | 0.65 |
| stays | 0.15 |
| comes | 0.10 |
| pours | 0.06 |
| goes | 0.04 |
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Using LLMs for compression is similar to how they’re used to generate text, except that we don’t choose the next word. Why? Because we’re not trying to generate new text. We already know what the next word is! Here’s how it works: based on the previous tokens (i.e. based on the context), the model says, “These are the tokens I think come next, and their probabilities.” Then it looks at what the real next symbol is. Whatever probability the model assigned is what determines the cost, in bits. If the model is well-trained, the token it thinks has the highest probability will be the actual next symbol.
As you click through the demo, notice how the total bits (at the top) increases based on the probability for each token encoded. Again, the number of bits required to represent each token is determined by negative log base 2 of probability.
0.00 bits
Now, if the model is not well-trained, it pays a price. For example, if our context is “The rain in”, a poorly trained model might give “Bermuda” a probability of 0.82, but the actual next word is “Spain”, which it assigned a probability of 0.02. Remember, lower probabilities require more bits, so the model is dinged for guessing wrong:
We can see these differences with arithmetic coding as well. Remember: when we encode each symbol, we’re left with a smaller and smaller range. Encoding symbols with small probabilities (like when our model makes poor guesses) makes our ranges even tinier. Our final number needs to fit inside those ranges, and the smaller the range, the more precision is needed. More precision = more digits = more bits.
Two mock arithmetic-coding diagrams. The good model keeps encoding a high-probability slice, so its range stays wide across three levels. The bad model keeps encoding a low-probability slice, so its range collapses to a sliver.
Good model
The final range encodes to the number 0.61328125
(9 bits)
Bad model
The final range encodes to the number 0.8193759582936763763427734375
(28 bits)
That said, even archaic LLMs that are considered crappy by today’s standards can achieve some impressive compression ratios. Here’s how an order-1 model stacks up against GPT-2 with arithmetic coding in compressing a famous Charles Dickens quote:
“It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness.”
So if LLMs are so great at compression, why aren’t we using them everywhere?
Unfortunately, how good a model is at compressing alone doesn’t give us the full picture. See, the goal of compression tools isn’t just to shrink data as much as possible. It’s to shrink the data as much as possible, given certain resource constraints.
Take HTTP responses: when your browser requests a webpage, it sends a header like Accept-Encoding: gzip, br
, telling the server which compression formats it can decode (gzip, Brotli, etc). The server picks one to compress the response before sending it.
Let’s assume a server uses gzip to compress its response. When your browser receives this response, it uses a small, built-in model to decode the gzip-compressed bitstream into HTML, CSS, and JavaScript. The overhead is tiny. If we were to instead use an LLM for this job, both the browser and the server would need a copy of the LLM, which could be multi-gigabytes large. That’s a high price for good compression, and we haven’t even run the thing. Compressing (and decompressing) data would demand a lot of resources and degrade page load speed to an unusable degree. Imagine: for every stylesheet, every script, every JSON payload running an LLM to compress and decompress. Yuck.
For a task as trivial as squishing HTTP responses, LLMs are comically overkill: once you factor in the model’s size, you’d be shipping gigabytes to save a few KB. But even if you were trying to compress datasets that dwarf the size of the LLM, the astronomical amount of compute required would still make this impractical.
Compressing data down to its entropy is, at this point, a solved problem. Arithmetic coding, developed in the late 1970s, lands within a couple bits of the limit, and these days entropy coders compete on speed and memory, not ratio.
The open question is how small we can make our entropy. Better models—better predictors—help us lower this number. LLMs are fantastic at this (setting aside the overhead cost), but what’s really interesting is that they’re trained to minimize that exact bits-per-symbol number. With LLMs this is called cross-entropy, but it’s the same underlying formula. So while in compression entropy measures how small we can shrink things, in language modeling, it’s a number we reduce to make our model better at prediction. If you’d like to dig into the nitty-gritty of this, check out this article by Chris Olah.
At the end of the day, though, both LLMs and compression algorithms are predictors. They’re two expressions of the same underlying math. Compression is prediction, and LLMs are compressors.
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