Neural LLM architecture

Imagine you're writing a long story. You might keep a notebook where you jot down important details about the characters, plot points, and settings. This allows you to quickly refer back to these details as you write, rather than having to reread the entire story every time you need to remember something.

Key Value (KV) cache is a key factor in the performance of many LLMs, but it also needs to be carefully managed to avoid excessive memory usage.

Here's an explanation of KV cache:

• Core Component: It's a critical component of transformer models, a type of neural network architecture used in many large language models (LLMs). 
• Purpose: To store and retrieve previously computed data during the generation of text or other sequential data. This helps the model generate responses quickly without needing to recalculate information it has already processed.
• How it Works:
  • Keys and Values: For each token (word or part of a word) in the input text, the model generates a "key" and a "value". These are essentially vectors (mathematical representations) that capture the meaning and context of the token.
  • Storage: These keys and values are stored in the KV cache.
  • Retrieval: When generating new text, the model can refer back to the cached keys and values to understand the context of what has been generated so far. 

Why is it Important?

• Efficiency: KV caching significantly reduces the computational cost of generating text. Without it, the model would have to recompute the keys and values for every token at every step, which would be extremely slow.  
• Memory Usage: While it improves efficiency, it can also lead to high memory usage, as the cache needs to store a large amount of data.  

It's a crucial mechanism in transformer models that enables them to generate text efficiently by storing and retrieving previously computed data.