What Are Weights? Everything a Model Knows How to Do
The file that months of training finally condenses into: what it looks like, how big it is, and why holding the weights means holding control
THE QUESTION THIS PAGE ANSWERS
ANSWER FIRSTWhat Are Weights? Everything a Model Knows How to Do?
The file that months of training finally condenses into: what it looks like, how big it is, and why holding the weights means holding control
Make the claim earn its place. Use this page as a decision aid, not a definition to memorize. Connect the idea to one real task, one observable result, and one failure that would change your mind.
Write one question you could answer with evidence after trying this idea.
A conclusion that sounds complete but leaves the key assumption untested.
Almost every example in this chapter comes from Qwen, so let us say why up front.
- It covers the size range most completely. In the official Ollama library the Qwen3.5 generation runs from 0.8B all the way up to 122B, with 2B, 4B, 9B, 27B and 35B in between. Every hardware tier discussed later maps onto a file you can genuinely download, without switching brands to fill the gaps.
- Other people build on top of it. Four of the six distilled versions DeepSeek released for R1 use a Qwen base — that is a third party's choice, not a vendor's own marketing.
- It also serves as its own cautionary tale. During the Qwen2.5 era the 3B and 72B models shipped under a non-standard license, which makes it a useful illustration of how unreliable the word "open source" can be. That discussion is in lesson 2.
In the Chinese-speaking community some people call it "the god of open source". That is a nickname, not a rating, and it does not mean Qwen leads on every dimension. This chapter uses it for examples because the material is complete and easy to work through, not because it is the best. What you should walk away with is a set of criteria, not a brand name — the same criteria should work just as well applied to any other vendor.
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.
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.
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:
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.
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.
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.
Put all of that together and the significance of weights is clear. They are not just a file. They are control.
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.
Put “The short answer” back into its constraints
“Almost every example in this chapter comes from Qwen, so let us say why up front” shows that a model, license, access route, or leaderboard is information—not an answer outside context. The real choice depends on task, data boundary, latency, quality floor, and operating cost.
Write elimination criteria before chasing the top score
The comparison in “In the Chinese-speaking community some people call it "the god of open source" .” should use the same real inputs while observing correctness, failure behavior, response time, and cost. A model leading a public leaderboard may still fail your license, privacy, or peak-latency constraints.
- Other people build on top of it. Four of the six distilled versions DeepSeek released for R1 use a Qwen base — that is a third party's choice, not a vendor's own marketing
Without a test set, there is no reliable winner
Start with “Turn that around: with an API-only model, what you rent is the right to use it.”: choose inputs that could genuinely change the decision and write down one counterexample that would reverse your choice. That is more useful than memorizing a single ranking.
From “The short answer” to “What it looks like”
“The short answer” grounds the problem in “Almost every example in this chapter comes from Qwen, so let us say why up front”. “What it looks like” then moves it toward “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 val…”. Together, they show that the lesson is not just a conclusion to remember, but a claim with conditions.
Carry the judgment into the next situation
For model selection, write non-negotiable constraints from the real task first. Compare quality, failure behavior, latency, licensing, and cost on the same inputs; use a leaderboard only as a starting point.
- “The short answer”: Almost every example in this chapter comes from Qwen, so let us say why up front
- “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 val…
- “The closing point”: Other people build on top of it. Four of the six distilled versions DeepSeek released for R1 use a Qwen base — that is a third party's choice, not a vendor's own marketing
The final “The closing point” brings the discussion to “Other people build on top of it. Four of the six distilled versions DeepSeek released for R1 use a Qwen base — that is a third party's choice, not a vendor's own marketing”. The useful thing to carry forward is knowing which judgments must be revisited when input, scale, or risk changes.
I turned one judgment from this article into a small experiment I could run today. Knowing what to observe next is more useful than simply remembering the conclusion.
After reading this, I first looked for the conditions behind the idea instead of copying the method into a project. That order made the later trade-offs much clearer.
When this judgment reaches real work, which constraint should be added first? I am curious which step matters most between reading and the first practical attempt.
No discussion on this article yet.