RL Infrastructure
Explain rollout workers, policy updates, weight freshness, placement, checkpoints, and operational metrics.
Overview
InfraLens Interview Practice
Practice the system explanation inside the page.
Cross-topic Practice
Explain mechanisms, tradeoffs, and failure modes out loud.
Interview practice is the verbal layer of InfraLens. It does not replace the handbooks or labs; it checks whether you can turn the system map into a concise explanation.
Source note
This interview system uses the public ai-infra-engineer-learning curriculum as inspiration for coverage. Answers are rewritten and reorganized for this site's handbook/interview format.
New Deep Dives
Practice newly integrated runtime domains
These topics extend the knowledge graph with handbooks, annotated labs, estimation tools, and rehearsable system explanations.
Multimodal Serving
Explain heterogeneous model stages, streaming outputs, memory transfer, and failure isolation.
Systems Runtime
Explain ownership, bounded queues, backpressure, transports, collectives, and timeout diagnosis.
Coding Practice
Derive memory and communication costs, then verify the estimate with small runnable exercises.
Related: handbook · estimators
Evidence policy
Claims in these tracks are anchored to official documentation or primary papers. Version-sensitive runtime behavior is framed as something to measure against the deployed stack.
Topic Organization
Practice by system area
Each topic card tells you what kind of explanation it trains and where to review the underlying handbook or lab material.
AI Infra
Trains platform-level explanations: compute, memory, communication, deployment, observability, cost, and failure handling.
Related: AI Infra handbook · AI Infra labs
Distributed Training
Trains explanations of DDP, FSDP, topology, collectives, and multi-node failure modes.
Related: Distributed handbook · Distributed labs
Transformer
Trains explanations of QKV, causal attention, RoPE, FFN, KV Cache, attention backends, and inference memory.
Related: Transformer handbook · Transformer labs
GPU Systems
Trains explanations of CUDA memory, profiling, mixed precision, FlashAttention, and deployment constraints.
Related: GPU kernels handbook · AI Infra labs
Model Serving
Trains explanations of batching, KV Cache, vLLM-style serving concepts, RAG, monitoring, and autoscaling.
Related: Serving handbook · Serving lab
Method
How to use this section
Read a question, answer out loud, then compare against the short answer and deeper explanation.
- Start from the related handbook section if the concept is unfamiliar.
- Use the Q&A card to rehearse a 30-60 second explanation.
- Open the related lab when you need to connect the explanation to code, tensor shape, or resource behavior.