Distributed AI Systems & Inference at Scale
This section covers the intersection of distributed systems and large language models — the infrastructure, algorithms, and engineering decisions that make AI work at planetary scale.
Files
| File | Topics | Key Interview Themes |
|---|---|---|
| distributed-ai-infra.md | Agent execution, GPU scheduling, model placement, federated training, edge inference | “How do you run 10k agents in parallel?” |
| distributed-rag.md | Vector DB scaling, semantic caching, multi-tenant RAG, inference queues | “How do you serve RAG to 1M users?” |
| disaggregated-inference.md | Prefill/decode split, KV cache disaggregation, RDMA/CXL inference | “Why split prefill and decode?” |
| distributed-training.md | Collective comms, fault-tolerant training, federated learning, Byzantine robustness | “How does GPU training scale to 16k GPUs?” |
Why This Matters for Interviews
Modern AI systems are distributed systems problems. Every company running LLMs at production scale faces:
- Resource fragmentation — GPUs are expensive and rarely fully utilized.
- Communication overhead — all-reduce on thousands of GPUs dominates training time.
- Latency vs. throughput — serving users in real-time while maximizing GPU utilization.
- Fault tolerance — a single GPU failure shouldn’t kill a 3-week training run.
Understanding these trade-offs signals senior-level systems thinking. Expect questions that start with “how would you design…” and require combining ML knowledge with distributed systems fundamentals.
Prerequisites
- Basic transformer architecture (see transformer-internals.md)
- Inference systems basics (see inference-systems.md)
- Distributed systems fundamentals (Paxos/Raft, consensus, network models)
- Kubernetes and container orchestration fundamentals