Building LLMs From the Metal Up: A Systems Engineer's Journey

In an AI landscape dominated by opaque, million-line codebases, a new educational initiative is empowering engineers to reclaim understanding. tiny-llm, created by systems engineers Chi (Neon/Databricks) and Connor (PingCAP), strips away the complexity obscuring large language model serving. As they note in their course preface: "Most open-source LLM projects are highly optimized with CUDA kernels... it's not easy to understand the whole picture."

Why Scrap the Stack?

• The Abstraction Problem: Production LLM serving relies on layers of optimizations (CUDA kernels, distributed systems) that obscure core mathematical operations
• The Hardware Barrier: NVIDIA GPU shortages make Apple Silicon an accessible alternative via MLX—Apple's array framework for machine learning
• The Learning Gap: Existing resources focus on model usage rather than implementation, leaving systems engineers without foundational knowledge

Course Architecture: Three Weeks From Zero to Optimized Serving

1. WEEK 1 - FOUNDATIONS
   » Serve Qwen2-7B using pure Python matrix operations
   » Implement transformer architecture without frameworks
   » Focus: Mathematical fundamentals of inference

2. WEEK 2 - KERNEL OPTIMIZATION
   » Replace Python ops with custom C++/Metal kernels
   » Leverage Apple Silicon GPU acceleration
   » Focus: Bridging algorithms to hardware

3. WEEK 3 - SYSTEMS THINKING
   » Implement request batching for throughput
   » Design serving architecture tradeoffs
   » Focus: Production-grade performance

The Systems Engineer's Advantage

Unlike typical ML courses, tiny-llm assumes backgrounds in systems—not data science. Prerequisites include:
- CMU's Deep Learning Systems course (PyTorch internals)
- Experience with low-level performance concepts
- Comfort with dimensional reasoning (tensor shapes, memory layouts)

"We unify dimension symbols across all materials so you're not deciphering what 'H, L, E' mean in every paper," the authors emphasize. This systems-first approach enables precise optimization targeting—whether rewriting Python in Metal or designing batched inference.

Why This Matters Now

As enterprises deploy LLMs at scale, understanding inference becomes critical for:
- Cost optimization (hardware/compute tradeoffs)
- Latency reduction
- Security hardening
- Custom architecture modifications

tiny-llm's open-source approach (CC BY-NC-SA 4.0) and Discord community create a rare space for collaborative systems-deep learning exploration. For engineers tired of treating LLMs as black boxes, this is your invitation to rebuild them from first principles.

Source: skyzh/tiny-llm