Understanding LLM Model Sizes and Hardware Requirements
Chapter 1: Introduction to Large Language Models and Size
What is a Large Language Model (LLM)?
Understanding Model Parameters
How Model Size is Measured
Examples of Different Model Sizes
Chapter 2: Essential Hardware Components for AI
The Central Processing Unit (CPU)
Random Access Memory (RAM)
The Graphics Processing Unit (GPU)
Video RAM (VRAM)
Brief Overview of TPUs
Chapter 3: Connecting Model Size to Hardware Needs
Model Parameters and Memory Consumption
Data Types and Precision (FP16, INT8)
Introduction to Quantization
Compute Requirements (FLOPS)
Memory Bandwidth Importance
Chapter 4: Running LLMs: Inference vs. Training
What is Model Inference?
Hardware Needs for Inference
What is Model Training?
Hardware Needs for Training
Focus on Inference Requirements
Chapter 5: Estimating Hardware Needs
Rule of Thumb: Parameters to VRAM
Accounting for Activation Memory
Factors Influencing Actual Usage
Checking Hardware Specifications
Practice: Simple VRAM Estimations
Introduction to Large Language Models and Size
This chapter introduces Large Language Models (LLMs) and the concept of model size. We will begin by defining what an LLM is and its fundamental purpose in processing and generating human language.
You will learn about the core building blocks of these models: parameters. We will explain what parameters represent and why they are the standard metric for measuring the size of an LLM. This includes understanding common units like billions of parameters (B). Finally, we'll look at examples of different LLMs to provide context for various model sizes you might encounter.
Sections
1.1 What is a Large Language Model (LLM)?
1.2 Understanding Model Parameters
1.3 How Model Size is Measured
1.4 Examples of Different Model Sizes
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