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
Memory Bandwidth Importance
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- CUDA C++ Programming Guide, NVIDIA Corporation, 2023 (NVIDIA Corporation) - Explains GPU memory structure, including global memory and memory access methods, which are essential for understanding data transfer speed.
- HBM3: The Next-Gen Memory Standard for AI and HPC, NVIDIA Corporation, 2022 (NVIDIA Corporation) - Describes High Bandwidth Memory (HBM) technology, its design, and why it is beneficial for high-performance computing tasks like AI and large language models.
- NVIDIA TensorRT-LLM: An Open-Source Library for Accelerating LLM Inference on NVIDIA GPUs, NVIDIA Corporation, 2023 (NVIDIA Corporation) - While focusing on an acceleration library, this article highlights the performance challenges of large language model inference, indirectly illustrating the importance of efficient data movement and memory bandwidth.
- Computer Architecture: A Quantitative Approach, John L. Hennessy, David A. Patterson, 2017 (Morgan Kaufmann) - Provides foundational concepts of computer architecture, including memory hierarchy, data transfer rate, and latency, which inform the understanding of GPU performance limits.