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
Model Parameters and Memory Consumption
Was this section helpful?
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - This book provides a comprehensive theoretical and practical foundation for deep learning, including the role of parameters in neural networks and the computational aspects of training and inference, which are fundamental to understanding memory consumption.
- Attention Is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin, 2017 Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc.) DOI: 10.5591/978-1-57766-302-3.375 - The seminal paper introducing the Transformer architecture, which forms the basis for most Large Language Models. Understanding the structure of Transformers helps to grasp why these models have such a vast number of parameters and, consequently, high memory demands.
- CUDA C++ Programming Guide, NVIDIA Corporation, Latest Edition (NVIDIA Corporation) - Provides detailed information on NVIDIA GPU architecture, including the hierarchy and characteristics of device memory (VRAM) and its role in high-performance computing for applications like deep learning.
- CS224N: Natural Language Processing with Deep Learning, Diyi Yang, Tatsunori Hashimoto, 2025 (Stanford University) - Provides lecture materials and assignments that discuss the computational requirements and practical considerations for training and deploying large language models, including discussions on memory.
- Scaling Laws for Neural Language Models, Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, Dario Amodei, 2020 arXiv preprint arXiv:2001.08361 DOI: 10.48550/arXiv.2001.08361 - This paper directly explores how model parameters affect performance and, by extension, the computational and memory resources required for effective LLM operation.