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
Examples of Different Model Sizes
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- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, 2019 Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (Association for Computational Linguistics) DOI: 10.18653/v1/N19-1423 - Introduces BERT, a foundational model for contextual embeddings, including its 'Large' variant which is an example of a smaller LLM by modern standards.
- Language Models are Few-Shot Learners, Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei, 2020 arXiv DOI: 10.48550/arXiv.2005.14165 - Describes the architecture and scale of GPT-3, a seminal large language model with 175 billion parameters, showcasing its few-shot learning abilities.
- Mistral 7B, Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed, 2023 arXiv preprint DOI: 10.48550/arXiv.2310.06825 - Presents the Mistral 7B model, an example of a highly efficient and capable model within the smaller-to-medium parameter range.