Open Source Series · What Open Source Actually Opens

What Are Weights? Everything a Model Knows

The news announces another open-sourced model every week. What exactly is being opened? The code? Mostly it is one file: the weights. Once you know what that file is, you can judge every open-source argument that follows on your own.

The short answer
Weights are the pile of numbers a model is left with after training. The tens of millions of dollars and the months of compute all end up condensed into this one file. Get hold of it and you can run exactly the same model on your own machine — no network connection, no payment to anyone.
What it looks like

There are no rules inside a model, no knowledge base, no if-else. There is only an enormous table of numbers. Every number in the cells below is one weight, and the darker the color, the larger its absolute value.

Positive Negative Darker means a larger absolute value

That is 36 numbers. A Qwen3-8B model has 8 billion of them. The entire point of training is to nudge those billions of numbers from random values toward the right ones. Once that is done, the parameters are frozen and the model is fixed.

This also explains something from Chapter One: a model learns nothing while it is chatting with you. Weights are fixed during training; during a conversation they are read-only. Whatever you tell it is gone by next time.
Parameter count determines file size

How many bytes each weight takes depends on the storage precision. Models are usually released in FP16, which means 2 bytes per number. That gives you an easy estimate for file size:

Weight file size (GB) ≈ parameter count (B) × 2
8 billion parameters × 2 bytes = 16 billion bytes ≈ 16 GB

You can memorize the formula, but what it means only lands once you drag it yourself. The slider below runs from 0.5B up to 2.4 trillion, and you can switch storage precision as you go to see the same model's file swell and shrink.

Drag the parameter count to 8B, then switch once between FP16 and INT4.
Parameters 8B
Precision
16 GB

Here is the comparison across a set of real models. Qwen serves as the yardstick because its size lineup is currently the most complete — public releases run from 0.6B all the way to 2.4T — so comparing within one family keeps architectural differences out of the picture.

Qwen3-0.6Babout 1.2 GB
Qwen3-8Babout 16 GB
Qwen3-32Babout 64 GB
Qwen3-235B-A22Babout 470 GB
Qwen3.8-Max (2.4T parameters)several TB
Parameter counts come from each model's official release notes; sizes are theoretical FP16 estimates, and actual files differ slightly depending on how they are sharded and which config files ship alongside them. Qwen3.8-Max has 2.4T total parameters with 95B active, released 2026-08-03.
One thing people mix up

Storage size and runtime footprint are two different things. A 16 GB file does not mean 16 GB of VRAM will run it. At runtime the model also needs room to hold the intermediate state of the conversation — the KV Cache from Chapter Two. In practice, budget another 30% to 50% on top of the parameter size.

There is an interactive tool at the end of this chapter where you can pick your own GPU or Mac model and see what will run. For now, keep the conclusion: estimating VRAM from file size will leave you short.

Why weights matter so much

Put all of that together and the significance of weights is clear. They are not just a file. They are control.

Offline
No dependence on any provider
It runs with the network cable unplugged. Price hikes, rate limits, and deprecations cannot touch you
Editable
You can keep training on top of it
Fine-tune your own version on your own data, which is something an API call cannot do
Private
Data never leaves your machine
For medical records, legal files, and internal code that cannot go out the door, this is the only path

Turn that around: with an API-only model, what you rent is the right to use it. Price, availability, and the retirement date are all decided by the other side. During technology selection that difference turns directly into risk, and it is what the next section takes apart: "open source" means very different things depending on who is saying it.