← 🖥️ ML Infrastructure & Serving
Core
Inference Serving (vLLM, TGI)
Serving LLMs at high throughput under a latency SLO is its own engineering problem. Continuous batching keeps the GPU busy across requests of different lengths, and PagedAttention stops the KV cache from wasting memory. Naive one-request-at-a-time serving leaves most of an expensive GPU idle, which is why purpose-built runtimes like vLLM and TGI exist.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
LLM & GenAI FundamentalsWalk me through what actually happens when an LLM generates a response.→MLOps & ML EngineeringWhat problem does a feature store solve? Explain the online store versus the offline store.→AI Security, Privacy & GovernanceDesign safety guardrails into the request flow of an LLM serving system, where do they sit and what does each catch?→MLOps & ML EngineeringWhat is training-serving skew, and how do you keep online and offline features consistent?→LLM & GenAI FundamentalsA customer says your LLM app is too slow. Give me five levers to reduce latency, and their tradeoffs.→ML Infrastructure & GPUsWhat is the KV cache, why does it dominate serving memory, and how do you size it? Do the math for a 70B model.→