# LLM Architecture: Lessons from Karpathy

> Source: <https://promptcube3.com/en/threads/3829/>
> Published: 2026-07-26 20:01:17+00:00

# LLM Architecture: Lessons from Karpathy

Stop thinking of LLMs as "thinking" machines and start seeing them as massive autocomplete engines. I finally got around to Karpathy's deep dive into how

The most important distinction is that a base model is just a stochastic text generator. It has no concept of being an "assistant." It's trained on the internet to predict the next token, period. To make it "chat-able," it goes through post-training (SFT/RLHF) where it's fed human conversation examples. This is why a base model might respond to a question with more questions (because it thinks it's completing a FAQ list) rather than actually answering you.

The process is essentially a massive game of reducing "loss."

When OpenAI or

One weird takeaway: LLMs are fundamentally bad at spelling or counting (like the "how many r's in strawberry" meme) because they process tokens, not individual characters. They aren't "looking" at the letters; they are predicting the next token based on probability.

[ChatGPT](/en/tags/chatgpt/)-style models actually work, and it clears up a lot of the mystery regarding why certain models fail at simple tasks while crushing complex coding benchmarks.As someone who leans on [Claude](/en/tags/claude/) Code for the heavy lifting—Jira tickets, unit tests, and architecture—and uses ChatGPT for research, I wanted to know what actually changes when a "new version" drops.

Here is the technical breakdown of the core concepts:

## Base Models vs. Instruct Models

The most important distinction is that a base model is just a stochastic text generator. It has no concept of being an "assistant." It's trained on the internet to predict the next token, period. To make it "chat-able," it goes through post-training (SFT/RLHF) where it's fed human conversation examples. This is why a base model might respond to a question with more questions (because it thinks it's completing a FAQ list) rather than actually answering you.

## The Training Grind

The process is essentially a massive game of reducing "loss."

**The Goal:** Lower the loss number.**The Process:** The network makes a prediction, calculates the error (loss), and recomputes parameters to be slightly more accurate next time.**Hardware:** This is why GPUs are non-negotiable. CPUs can't handle the massive parallel matrix multiplication required for these neural networks. While Karpathy focuses on GPUs, TPUs are obviously the other heavy hitter for tensor operations.

## What "New Model" Actually Means

When OpenAI or

[DeepSeek](/en/tags/deepseek/)announce a release, they are usually talking about two different things:**Open Weights:** You get the forward-pass code and the parameters (a file with billions of numbers).**Closed Source:** You get an API endpoint or a UI. You have zero visibility into the weights or the specific architecture.

One weird takeaway: LLMs are fundamentally bad at spelling or counting (like the "how many r's in strawberry" meme) because they process tokens, not individual characters. They aren't "looking" at the letters; they are predicting the next token based on probability.

If you're looking for a real-world AI workflow, understanding this helps you realize when to stop fighting a model's inherent architecture and start adjusting your prompt engineering.

[Next OpenTelemetry + SigNoz: Tracing Streaming Voice AI →](/en/threads/3821/)

## All Replies （3）

N

Still sounds like a fancy guess. Where's the actual proof this isn't just statistical noise?

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J

Treating prompts like a pattern to complete usually gets me much better results.

0

A

Took me forever to click with this, but thinking of it as token prediction changed everything.

0