# How Mogemma Works: A Guide for Systems Engineers

Welcome to Mogemma! If you are a systems engineer, backend developer, or systems architect who knows how memory, networking, and CPUs work but feels a bit lost in the "AI jargon" of transformers, this guide is for you.

We are going to demystify what Large Language Model (LLM) inference actually is, and how Mogemma implements it using Mojo to run Gemma 3 securely and efficiently.

## The Core Concept: Next-Token Prediction
At its absolute simplest, an LLM is a very large, very complex statistical function that does exactly one thing: **predict the next word (token).**

When you ask a model:
> "What is the capital of France?"

The model doesn't "think" about Paris. Instead, it looks at the sequence of words: `["What", "is", "the", "capital", "of", "France", "?"]` and calculates probabilities for what the *next* token should be. It might determine:
- `Paris` (98% probability)
- `The` (1% probability)
- `London` (0.1% probability)

It selects "Paris", appends it to the sequence, and then runs the *entire* function again on `["What", "is", "the", "capital", "of", "France", "?", "Paris"]` to predict the next token (which might be `is` or `<end_of_sequence>`).

This process of looping repeatedly to generate one word at a time is called **Autoregressive Generation**.

## Tokens and Embeddings
Models don't actually read English text. Before text enters the math engine, it goes through a **Tokenizer**.

1. **Tokenizer:** A dictionary that maps chunks of text into integers. For example, "Hello world" might become `[341, 8090]`.
2. **Embedding:** Once we have an integer (e.g., `341`), we look it up in an "Embedding Table" (a giant matrix). This converts the integer into a dense vector of floating-point numbers (e.g., `[0.12, -0.45, 0.88, ...]`). 

This vector represents the "meaning" of the token in high-dimensional space. From this point on, the entire model is just doing math on floating-point vectors.

## The Transformer Layer
The "brain" of the model is a stack of **Transformer Layers** (Gemma 3 has dozens of them stacked on top of each other). A vector goes into Layer 1, gets mutated, goes into Layer 2, gets mutated again, and so on.

Each layer has two main components:

### 1. Self-Attention (The Context Mechanism)
If the token is "bank", does it mean a river bank or a financial bank? The word itself doesn't know. 

**Self-Attention** is the mathematical mechanism where a token looks at *all the other tokens* that came before it in the sequence to gather context. It does this by creating three new vectors for each token:
- **Query (Q):** "What kind of context am I looking for?"
- **Key (K):** "What kind of context do I provide?"
- **Value (V):** "If you match with my Key, here is the actual data I hold."

The model multiplies the Query of the current token against the Keys of all previous tokens. High scores mean high relevance. It then blends the Values of those relevant tokens into its own vector.

### 2. The MLP (The Fact Mechanism)
After Self-Attention updates the vector with contextual awareness, it passes through a Multi-Layer Perceptron (MLP). This is a standard neural network block that acts like a key-value store of "facts" the model memorized during training (e.g., "Paris is the capital of France"). 

## The KV Cache: Optimizing the Loop
Remember how the model loops to predict one token at a time? 

If the sequence is 1,000 tokens long, doing Self-Attention means multiplying the current Query against 1,000 previous Keys. If we recalculate those 1,000 Keys from scratch every single loop, the engine will be incredibly slow.

**The KV Cache (Key-Value Cache)** is the most important systems engineering concept in LLM inference. 
Because the Keys and Values for previous tokens *never change*, we calculate them once and save them in memory (the cache). 

When generating token #1001:
1. We only run the math for token #1001 to get its Q, K, and V.
2. We append its K and V to the KV Cache.
3. We multiply its Q against the cached K's (tokens 1 through 1000).

Mogemma manages this memory pool securely. A large chunk of RAM is allocated specifically to hold this growing sequence of vectors as the conversation continues.

## Multimodal Vision
Mogemma isn't just for text! Gemma 3 is a multimodal model, meaning it can understand images. 

Images aren't text, so they can't go through the text tokenizer. Instead, Mogemma uses a **Vision Tower** (a specific type of Transformer called SigLIP).
1. We take an image (e.g., a 384x384 JPEG).
2. We chop it up into a grid of tiny patches (e.g., 14x14 pixel squares).
3. We run mathematical convolutions to turn each patch into a vector.
4. We pass those vectors through the Vision Tower to extract the "visual meaning."
5. We inject those visual vectors directly into the text stream alongside the text embeddings.

To the main text transformer layers, a picture of a dog just looks like a sequence of highly descriptive vectors!

## Why Mojo?
Python is great for writing APIs, but it is notoriously slow for running billions of floating-point operations per second. Standard LLM engines (like llama.cpp) use C++ or CUDA kernels wrapped in Python bindings.

Mogemma uses **Mojo**, a language designed to have the syntax of Python but the performance and memory control of C/Rust. 
- All the intense math (`mat_mat_mul`, Self-Attention, KV Caching) is written in pure Mojo.
- Mojo leverages SIMD (Single Instruction, Multiple Data) to crunch arrays of floats simultaneously on your CPU.
- Python simply acts as the high-level API orchestrator, passing memory pointers down to the Mojo core. 

This results in a single, lightweight binary that runs incredibly fast natively on the CPU without requiring massive external C++ frameworks.