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
Quiz for Chapter 1
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
Quiz for Chapter 2
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
Quiz for Chapter 3
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
Quiz for Chapter 4
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
Quiz for Chapter 5
Understanding LLM Model Sizes and Hardware Requirements
Prerequisites: No prior knowledge
Level:
Beginner
What You'll Learn
LLM Fundamentals
Define what a Large Language Model (LLM) is and its basic function.
Model Parameters
Explain what model parameters are and how they relate to LLM size.
Hardware Components
Identify the main hardware components (CPU, GPU, RAM, VRAM) involved in running LLMs.
Size-Hardware Relationship
Describe the connection between the number of parameters in an LLM and the required hardware resources.
Inference vs. Training Needs
Distinguish between the hardware demands for running LLM inference versus training (at a high level).
Basic Requirement Estimation
Perform simple estimations for memory (VRAM) based on model size.