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
What is Model Training?
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A foundational textbook covering the mathematical and algorithmic principles of deep learning, including optimization, loss functions, and neural network training.
- A Survey of Large Language Models, Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, Ji-Rong Wen, 2023 arXiv preprint arXiv:2303.18223 DOI: 10.48550/arXiv.2303.18223 - Provides a comprehensive overview of large language models, including discussions on pre-training objectives, architectures, and general training methodologies.
- Cloud TPU for Large Language Model Training, Google Cloud Documentation, 2024 (Google Cloud) - Official documentation explaining how Google Cloud TPUs are utilized for large-scale language model training, highlighting hardware and scaling considerations.
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer, Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu, 2020 J. Mach. Learn. Res., Vol. 21 (JMLR) - An influential paper introducing the T5 model and discussing transfer learning, pre-training objectives, and fine-tuning strategies for various NLP tasks.