Explains what a language model is, how Transformers and learned weights power model behavior, and why large models require significant memory and compute and are served via model servers
Let’s pull ChatGPT apart — from the box you type into all the way down to the thing actually running underneath. At the core is a model server: a program that runs a language model and answers requests over the network. Each new layer we add produces an answer to the problem posed by the layer above it.
What is ChatGPT, really?You open ChatGPT, type a question, hit Enter, and a moment later an answer comes back. The name splits into two distinct pieces:
The chat part is the application — the website, the text box, and the UI that shows replies. It’s what you see and interact with.
The GPT part is the model — the system that actually interprets your question and computes an answer. GPT is one implementation; other services use different models.
There are many models — Anthropic’s Claude, Google’s Gemini, Meta’s Llama — but the same basic idea underlies them all: an application wrapped around a model that does the “thinking.”Which leads to a core question:
What is a model?At its simplest, a model is a formula plus a set of numbers called weights.Imagine a tiny machine where you drop a number in and a different number comes out: drop in 3 and out comes 6; drop in 10 and out comes 20. That machine doubles the input. The “formula” is the multiplication, and the multiplier itself (2) is a weight.Example:
output = input * 2
Two parts: the fixed structure (the multiplication) and the learned parameter (the number 2). Change that number to 3 and the same formula now triples inputs.
That split — a fixed formula with plug-in weights — is the core idea, and it scales from tiny examples up to GPT.A single multiplier is not enough for complex tasks, so the formula becomes richer. Consider predicting house prices: features such as size, bedrooms, and age each contribute, with learned multipliers that indicate importance.
Those numbers (300, 50000, -1000, 25000) are weights. They aren’t guessed; they’re learned by training: presenting many examples (houses with features and sale prices) and optimizing the weights to fit the data.
So: a model is a formula, and what a model “knows” is encoded as learned weights. For simple tasks, a handful of weights suffices. For language, which is highly contextual and entangled, you need vastly more parameters and a richer formula: more inputs, more multiplications, and many layers where outputs of one layer feed into the next.
If you expand the structure in a particular way you reach the Transformer architecture used in modern language models. You don’t need full mathematical detail right now — the Transformer is a recipe for combining inputs with weights.
The Transformer is the formula: a fixed sequence of operations implemented once in code and reused across many models. In simplified pseudocode:
def transformer(tokens, weights): x = embed(tokens) for layer_params in weights: x = attend(x, layer_params) x = feed_forward(x, layer_params) return next_token(x)# same page of code for every model
The code (the Transformer recipe) is similar across GPT, Claude, and Llama. The differences come from the specific weight values. That’s the surprising and important part: the program that runs the model is relatively compact, while the model’s knowledge and behavior live in the weights — often billions of them.
Those billions of weights are stored as a very large file on disk. A trained model is literally a file filled with numbers. To run it, you load the file into memory (usually RAM and often GPU memory), feed text into the Transformer formula, and read the generated text back.
That is the essence of a running model: load weights into memory, perform math on inputs, and produce outputs.Why not run the biggest models on your laptop?If a model is “just a file” and running it is “just math,” why can’t everyone run the largest models locally? Because practical deployment requires three major resources:
Resource
Why it matters
Typical scale / notes
Memory footprint
Large models need many gigabytes (or hundreds) of memory to store weights; GPUs often hold model shards
Tens → hundreds of GB for high‑quality models; quantization reduces size
Compute throughput
Generating tokens requires many matrix multiplications; GPUs and TPUs accelerate this massively
Datacenter GPUs / accelerators provide the parallelism needed
Latency & throughput
Interactive apps need low latency and high concurrency; servers use batching and multiple instances
Production systems orchestrate replicas and batching strategies
Smaller, quantized models can run on laptops or phones but typically trade off capability, correctness, or generation quality. Production-grade models are kept loaded in memory on machines with large RAM and GPUs, with many instances orchestrated to handle user load — which is why you usually access them through a model server or hosted API.
A trained model is essentially a large file of learned numbers. The Transformer is the small, fixed recipe that consumes those numbers. Running large models requires sufficient memory and parallel compute (usually GPUs), which is why most production systems expose them via a model server.
