Foundations of LLM Inference Pipelines
Phase 3 shifts focus from training to serving models at scale; this day establishes core inference concepts so later optimization work has a measurable baseline. Understanding pipelines early prevents downstream bottlenecks when integrating inference into StartupTribunal workflows. The day matters because inference latency and cost directly determine product viability for real users.
Resources
- 20 min
- 15 min
Deliverable
journal entry in app/maku/BriefForm.tsx documenting first inference latency measurement on a local model
Quiz · 3 questions
1. Which component most directly controls batching behavior during inference?
2. Why might increasing batch size reduce latency up to a point but then increase it?
3. Describe one concrete change you would make to the current brief submission flow if inference latency exceeded 2 s.