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
Brief Overview of TPUs
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- A Cloud-Scale TPU for Training Large AI Models, Norman P. Jouppi, Cliff Young, Daniel S. Holt, David Agrawal, Ganesh Venkataramani, et al., 2023 Proceedings of the 50th Annual International Symposium on Computer Architecture (ISCA '23) (ACM) DOI: 10.1145/3579371.3582046 - Describes the architecture and performance of Google's fourth-generation Tensor Processing Unit (TPU v4), optimized for large-scale AI model training in the cloud.
- About TPUs, Google Cloud, 2024 (Google Cloud) - Official Google Cloud documentation providing an overview of TPUs, their different versions, and how they are utilized within the Google Cloud Platform ecosystem.
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