AI Systems Deep Dive: Transformers, Quantization & Inference
AI Systems Deep Dive: Transformers, Quantization & Inference
A comprehensive project-based learning journey to master AI/ML systems engineering, from understanding transformers at the mathematical level to building production-grade inference engines.
Learning Path Overview
This learning path is organized into 5 phases, each building on the previous:
| Phase | Focus | Projects | Skills Gained |
|---|---|---|---|
| Phase 1 | Attention & Memory | P01-P02 | Mathematical foundations, memory optimization |
| Phase 2 | Complete Architectures | P03-P04 | End-to-end transformers, advanced architectures |
| Phase 3 | Model Compression | P05-P06 | Quantization, efficient fine-tuning |
| Phase 4 | Inference Optimization | P07-P09 | Caching, batching, speculative decoding |
| Phase 5 | Production Systems | P10 | Full production inference engine |
Projects
Phase 1: Attention & Memory Optimization
| # | Project | Difficulty | Time | Key Concepts |
|---|---|---|---|---|
| 01 | Build Attention from Scratch | Intermediate | 1 week | Softmax, QKV, Multi-head attention |
| 02 | Implement Flash Attention | Advanced | 1-2 weeks | Tiling, online softmax, memory efficiency |
Phase 2: Complete Architectures
| # | Project | Difficulty | Time | Key Concepts |
|---|---|---|---|---|
| 03 | Build a Full Transformer | Advanced | 2-3 weeks | Encoder-decoder, training loop, GPT/BERT |
| 04 | Implement Sparse MoE Layer | Expert | 2 weeks | Mixture-of-Experts, gating, load balancing |
Phase 3: Model Compression
| # | Project | Difficulty | Time | Key Concepts |
|---|---|---|---|---|
| 05 | Implement Post-Training Quantization | Advanced | 2 weeks | INT8, calibration, GPTQ |
| 06 | Implement LoRA | Intermediate | 1-2 weeks | Low-rank adaptation, efficient fine-tuning |
Phase 4: Inference Optimization
| # | Project | Difficulty | Time | Key Concepts |
|---|---|---|---|---|
| 07 | Build a KV Cache | Advanced | 1 week | Caching, sliding window, streaming |
| 08 | Implement Continuous Batching | Master | 3-4 weeks | PagedAttention, vLLM, scheduling |
| 09 | Implement Speculative Decoding | Expert | 2 weeks | Draft/target models, rejection sampling |
Phase 5: Capstone
| # | Project | Difficulty | Time | Key Concepts |
|---|---|---|---|---|
| 10 | Production Inference Engine | Master | 2-3 months | CUDA, Rust, full integration |
Prerequisites
Before starting this learning path, you should have:
- Python proficiency: Comfortable with NumPy, PyTorch
- Linear algebra: Matrix operations, eigenvalues, SVD basics
- Calculus: Gradients, chain rule for backpropagation
- Deep learning basics: Neural networks, loss functions, optimizers
Recommended Study Order
- Week 1-2: Project 1 (Attention) - Build mathematical foundation
- Week 3-4: Project 2 (Flash Attention) - Learn memory optimization
- Week 5-7: Project 3 (Transformer) - Build complete architecture
- Week 8-10: Projects 5-6 (Quantization, LoRA) - Model compression
- Week 11-12: Project 7 (KV Cache) - Inference basics
- Week 13-16: Projects 8-9 (Batching, Speculative) - Advanced inference
- Week 17+: Project 10 (Production Engine) - Full system integration
Target Audience
- ML Engineers transitioning to AI infrastructure
- Software engineers building LLM applications
- Researchers wanting systems-level understanding
- Anyone building production AI systems
Technologies Used
| Category | Technologies |
|---|---|
| Languages | Python, Rust, C++, CUDA |
| Frameworks | PyTorch, Triton |
| Tools | vLLM, TensorRT-LLM, llama.cpp |
| Infrastructure | Docker, Kubernetes, Prometheus, Grafana |
Learning Outcomes
By completing this learning path, you will be able to:
- Implement transformers from scratch with full mathematical understanding
- Optimize memory usage using techniques like Flash Attention
- Compress models with quantization while maintaining quality
- Build high-performance inference systems rivaling commercial offerings
- Design production AI infrastructure with proper observability
How to Use These Projects
Each project file contains:
- Learning Objectives - What you’ll learn
- Theoretical Foundation - Deep conceptual coverage
- Project Specification - What to build
- Solution Architecture - How to design it
- Implementation Guide - Phased approach with hints
- Testing Strategy - How to verify correctness
- Common Pitfalls - Issues to avoid
- Extensions - Advanced challenges
- Interview Questions - Real-world preparation
- Resources - Books and papers
Contributing
This is a personal learning journey repository. Feel free to fork and adapt for your own learning path.
Part of the Learning Journey C project - A comprehensive approach to mastering AI systems engineering through project-based learning.