Week 13: Scaling & Cost-Efficiency

Introduction

As LLM-powered systems transition from research to production, efficient scaling becomes critical. This week explores the mathematical foundations of scaling laws, optimization techniques for inference (batching, caching, quantization), and cost-performance trade-offs that enable real-world deployment. Understanding these principles is essential for building systems that are both powerful and economically viable.

Goals for the Week

• Understand scaling laws that govern LLM performance, compute, and data requirements
• Master inference optimization techniques: batching, KV caching, and quantization
• Apply model compression strategies including distillation and parameter-efficient methods
• Evaluate cost-performance trade-offs across model selection and deployment strategies
• Build production-ready systems that balance quality, latency, and cost

Learning Guide

Videos

Short Courses:

• Efficiently Serving LLMs — DeepLearning.AI course by Travis Addair on production serving strategies
• Quantization Fundamentals — HuggingFace guide to model quantization techniques

Articles & Reading

Scaling Laws & Foundations:

• Scaling Laws for Neural Language Models (OpenAI)¹ — Foundational work on compute-performance relationships
• LLM Scaling Laws Explained — Cameron Wolfe’s comprehensive overview
• Chinchilla Scaling Laws² — Optimal compute-data trade-offs
• Understanding LLM Scalability Challenges — Practical deployment considerations

Inference Optimization:

• KV Caching Explained — HuggingFace guide to key-value caching
• Coding the KV Cache from Scratch — Sebastian Raschka’s implementation tutorial
• Continuous Batching for LLM Serving — Dynamic batching strategies
• PagedAttention: Efficient Memory Management³ — vLLM’s memory optimization technique

Quantization & Compression:

• Quantization Concepts Guide — HuggingFace comprehensive overview
• GPTQ: Post-Training Quantization⁴ — Efficient 4-bit quantization method
• AWQ: Activation-aware Weight Quantization⁵ — Improved quantization with minimal accuracy loss
• QLoRA: Efficient Fine-tuning⁶ — 4-bit quantized low-rank adaptation

Cost Optimization:

• FrugalGPT: Cost Reduction Strategies⁷ — LLM cascades and model selection
• FrugalGPT Implementation Guide — Practical application overview
• Benchmarking LLM Inference Costs — NVIDIA’s deployment analysis
• Batch Prompting for Efficient Inference⁸ — Reducing API costs through batching

Tools & Frameworks

Inference Engines:

• vLLM — High-throughput serving with PagedAttention and continuous batching
• Text Generation Inference (TGI) — HuggingFace production serving
• TensorRT-LLM — NVIDIA’s optimized inference library
• llama.cpp — Efficient CPU/GPU inference for Llama models

Quantization Tools:

• bitsandbytes — 4-bit and 8-bit quantization integration
• GPTQ — Post-training quantization implementation
• AutoGPTQ — Easy-to-use GPTQ wrapper
• AutoAWQ — Activation-aware quantization toolkit

Multi-LoRA Serving:

• LoRAX — Dynamic LoRA adapter serving
• vLLM LoRA Support — Multi-adapter inference
• TGI Multi-LoRA — HuggingFace adapter serving
• Triton LoRA Backend — Enterprise LoRA deployment

Commercial Platforms:

• OpenAI Batch API — Asynchronous batch processing with 50% cost reduction
• Anthropic Claude — Prompt caching and batch processing
• Google Gemini — Batch inference with Gemini

Programming Practice

Batch Processing & Optimization:

• Implement OpenAI Batch API workflows:
  • Send multiple prompts asynchronously
  • Measure cost savings (50% reduction) vs real-time API
  • Compare throughput and latency trade-offs
• Batch Processing Example — OpenAI cookbook implementation
• UPenn Batch API Tutorial — Academic workflow guide

Quantization Experiments:

• Quantize models using bitsandbytes:
  • Compare 4-bit, 8-bit, and FP16 performance
  • Benchmark memory usage, latency, and accuracy
  • Implement QLoRA for efficient fine-tuning
• Quantization with Transformers — HuggingFace Accelerate guide
• 4-bit Transformers Tutorial — Detailed implementation walkthrough

KV Cache Implementation:

• Build KV caching from scratch:
  • Implement attention with cached key-value pairs
  • Measure throughput improvements on repeated prompts
  • Experiment with cache eviction strategies
• Deploy with Redis or in-memory caching for production workflows

Inference Optimization:

• Deploy vLLM or TGI for production serving:
  • Configure continuous batching parameters
  • Benchmark requests/second and latency
  • Implement multi-LoRA serving for task-specific adapters
• Compare greedy decoding, beam search, and sampling strategies

Cost-Quality Analysis:

• Implement FrugalGPT-style cascading:
  • Route queries to cheaper models first
  • Escalate to larger models only when needed
  • Measure cost reduction vs accuracy trade-off
• Analyze token usage optimization through prompt compression

References