DAY 103 / 210
Core Techniques for LLM Inference Optimization
This day launches the inference phase by establishing foundational methods for reducing latency and memory use in deployed models. It matters because StartupTribunal's production systems will depend on these optimizations to deliver reliable, cost-effective AI features at scale.
⏱ 45 min target📝 3 quiz Qs
Resources
- 20 min
- 15 min
Deliverable
Journal entry with first inference latency benchmark results recorded in app/maku/BriefForm.tsx context
Quiz · 3 questions
1. Which technique primarily reduces memory bandwidth during autoregressive generation?
2. Name one common misconception when first measuring inference latency on a GPU.
3. How might the rate-limiter in lib/rate-limiter.ts interact with an inference optimization you choose to implement?