Case Studies
Design an LLM Pre-Training Pipeline
End-to-end system design interview walkthrough. Data curation, tokenization, distributed training, fault tolerance, scaling laws, and evaluation for pre-training a large language model.
Problem Statement & Requirements
Requirements & Scale Estimation
The Basics
System Architecture & Data Flow
Deep Dive: Data Pipeline
Data Pipeline & Quality Filtering
Deep Dive: Tokenization
Tokenizer Training & Design
Deep Dive: Model Architecture
Transformer Architecture Decisions
Deep Dive: Distributed Training
4D Parallelism & GPU Clusters
Deep Dive: Fault Tolerance
Checkpointing & Failure Recovery
Deep Dive: Scaling Laws
Scaling Laws & Monitoring
Deep Dive: Evaluation
Benchmarks & Contamination
Deep Dive: Cost & Operations
Cost Optimization & Launch Strategy