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
Hardware Needs for Training
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A comprehensive textbook covering the mathematical and conceptual foundations of deep learning, including backpropagation, optimization algorithms, and neural network training processes.
- CS231n: Convolutional Neural Networks for Visual Recognition (Course Notes), Fei-Fei Li, Ehsan Adeli, Justin Johnson, Zane Durante, 2025 Stanford University Course - Excellent online course notes from Stanford University that provide practical insights into training deep neural networks, including discussions on computational graphs, backpropagation, and hardware considerations.
- NVIDIA Deep Learning Documentation, NVIDIA Corporation, 2023 (NVIDIA) - Official documentation providing comprehensive guides on leveraging NVIDIA GPUs and related software for deep learning training, covering topics like hardware acceleration and distributed computing.