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
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
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
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
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
Connecting Model Size to Hardware Needs
Having introduced Large Language Models, their measurement in parameters, and the essential hardware components like GPUs and VRAM, we now connect these pieces. This chapter focuses on how the number of parameters in an LLM directly translates into hardware resource demands.
You will learn about:
- Memory Consumption: How model parameters are loaded into memory, primarily VRAM.
- Data Types and Precision: The impact of different numerical formats (like FP16, INT8) on memory usage.
- Quantization: A technique to reduce the model's memory footprint.
- Compute Requirements: Understanding the computational load (FLOPS) associated with model size.
- Memory Bandwidth: Why the speed of memory access is significant for LLM performance.
Sections
3.1 Model Parameters and Memory Consumption
3.2 Data Types and Precision (FP16, INT8)
3.3 Introduction to Quantization
3.4 Compute Requirements (FLOPS)
3.5 Memory Bandwidth Importance
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