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
Compute Requirements (FLOPS)
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A fundamental textbook that covers the mathematical foundations of neural networks, including matrix operations, which are central to LLM calculations.
- Programming Massively Parallel Processors: A Hands-on Approach, David B. Kirk and Wen-mei W. Hwu, 2016 (Elsevier) DOI: 10.1016/C2016-0-03708-1 - An authoritative resource explaining GPU architecture and parallel computing principles, clarifying why GPUs excel at the floating-point operations necessary for deep learning.
- NVIDIA Ampere Architecture In-Depth, Ronny Krashinsky, Olivier Giroux, Stephen Jones, Nick Stam, Sridhar Ramaswamy, 2020 NVIDIA Technical Blog (NVIDIA) - An official technical overview of a modern GPU architecture designed for AI, detailing its capabilities for high FLOPS throughput crucial for deep learning workloads.