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
The Graphics Processing Unit (GPU)
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- CUDA C++ Programming Guide, NVIDIA, 2024 (NVIDIA) - This official guide provides a detailed account of the CUDA architecture and its parallel programming model, which is central to understanding how GPUs execute many simple operations concurrently.
- ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, 2012 Advances in Neural Information Processing Systems (NIPS 2012), Vol. 25 (Curran Associates, Inc.) - A landmark paper that showcased the effectiveness of GPUs in accelerating deep learning models, significantly contributing to the widespread adoption of GPUs in AI research and applications.
- LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale, Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer, 2022 NeurIPS 2022 DOI: 10.48550/arXiv.2208.07339 - This research paper addresses the practical challenges of running large language models on GPUs, discussing techniques like 8-bit matrix multiplication to improve efficiency, directly relevant to GPU utilization for LLMs.
- Computer Architecture: A Quantitative Approach, John L. Hennessy and David A. Patterson, 2017 (Elsevier) - A well-regarded textbook that covers the fundamental principles of computer architecture, including detailed comparisons of CPU and GPU designs and the advantages of parallel processing for different workloads.