LLM From Scratch: Scaling & Optimization

From toy models to production-scale systems

Welcome to the Scaling & Optimization series, where we tackle the challenges of working with large language models: making attention faster, training 70B+ parameter models, handling 100K+ token contexts, shrinking models for inference, and deploying at scale.

After building a solid foundation in the core series and learning production techniques in the Production Techniques series, you’re ready for the advanced optimizations that make modern LLMs practical.

What You’ll Learn¶

This series covers cutting-edge techniques for scaling LLMs:

Optimizing attention with Flash Attention, KV cache, and multi-query attention
Splitting models across multiple GPUs with parallelism strategies
Extending context from 2K to 100K+ tokens with RoPE and ALiBi
Shrinking models 4-8× with quantization for fast inference
Serving at scale with continuous batching and speculative decoding

Prerequisites¶

You should understand:

Transformer architecture fundamentals (from core series)
Training loops and optimization basics
Ideally, the Production Techniques series

The Series¶

Lesson	Title	What You’ll Learn
L16	Attention Optimizations	Making attention 10× faster — Flash Attention, KV cache, Multi-Query/Grouped-Query Attention
L17	Model Parallelism	Training models too large for one GPU — Data, pipeline, tensor parallelism, ZeRO optimizer
L18	Long Context Handling	Extending from 2K to 100K+ tokens — RoPE, ALiBi, position interpolation, sparse attention
L19	Quantization for Inference	Shrink models 4-8× with minimal loss — INT8, INT4, GPTQ, AWQ techniques
L20	Deployment & Serving	Production-ready LLM serving — vLLM, continuous batching, speculative decoding, monitoring

Why These Topics Matter¶

The Scale Challenge¶

Modern LLMs face challenges that fundamentally change how we build them:

Challenge	Scale	Solution
Attention is $O(n^2)$	100K token context	L16: Flash Attention, sparse patterns
Model doesn’t fit in GPU	70B parameters	L17: Model parallelism, ZeRO
Trained on 2K, need 32K	Context extension	L18: RoPE, ALiBi, interpolation
14GB too large for edge	Memory constraints	L19: INT4 quantization (3.5 GB)
Sequential generation is slow	High throughput needs	L20: Continuous batching, speculation

Real-World Impact¶

These aren’t academic exercises—they’re the techniques that make modern LLMs possible:

GPT-4: Uses speculative decoding (L20) for faster responses
Llama 2: Uses Grouped Query Attention (L16) and RoPE (L18)
Claude: Extended to 100K context using position interpolation (L18)
Mixtral: Uses model parallelism (L17) to split 8× 7B experts
Most deployments: Use INT4 quantization (L19) to reduce costs

Learning Path¶

Sequential approach (recommended):

L16: Start with attention optimizations (most immediate impact)
L17: Learn to scale across GPUs
L18: Handle longer contexts
L19: Prepare models for efficient inference
L20: Deploy in production

Jump-in approach:

Need faster training? → L16 (Flash Attention) + L17 (Parallelism)
Need longer contexts? → L18 (RoPE, ALiBi)
Need cheaper inference? → L19 (Quantization) + L20 (vLLM)
Building production system? → L20 (Deployment)

What’s Different Here¶

Cutting-Edge Techniques¶

Unlike the core series (which focuses on timeless fundamentals), this series covers:

Techniques from 2023-2024 research
Industry practices not always in papers
Hardware-specific optimizations

System-Level Thinking¶

We move from:

“How does one layer work?” → “How does a 70B model fit across 8 GPUs?”
“How do I train one batch?” → “How do I serve 1000 requests/second?”

Trade-Off Analysis¶

Every technique has costs:

Flash Attention: Faster, but harder to debug
Quantization: Smaller, but slight quality loss
Parallelism: Scales up, but adds communication overhead

We’ll analyze these trade-offs explicitly.

Prerequisites Check¶

Before diving in, make sure you understand:

✅ Self-attention mechanism (Q, K, V) ✅ Multi-head attention ✅ Transformer architecture ✅ Training loops and gradient descent ✅ Basic GPU memory considerations

If any of these are unclear, review the core series first.

What You’ll Build¶

By the end of this series, you’ll be able to:

Optimize a 7B model to run 5× faster with Flash Attention + KV cache
Train a 70B model across 8 GPUs with model parallelism
Extend a 2K context model to 32K without retraining
Deploy a quantized model serving 100+ requests/second
Monitor and debug production LLM systems

Ready to Begin?¶

Let’s start by making attention—the core operation in transformers—10× faster with modern optimizations.

Next: L16 - Attention Optimizations →

Series Roadmap¶

Core Series (L01-L10)
└── Fundamentals: Build GPT from scratch
    └── Production Techniques (L11-L15)
        └── Real-world training and fine-tuning
            └── Scaling & Optimization (L16-L20) ← You are here
                └── Advanced techniques for scale

This series is recommended for those who have completed the Production Techniques series and want to push the boundaries of scale and performance.