← 🖥️ ML Infrastructure & Serving
Core
Model Serving Frameworks
You rarely build a serving stack from scratch; frameworks handle the production plumbing. General servers (Triton, TorchServe, KServe) serve many model types with dynamic batching, multi-model hosting, and versioning. LLM-specific servers (vLLM, TGI, TensorRT-LLM) add the essentials general servers lack: continuous batching, paged KV cache, and token streaming. Applied-AI interviews probe it because knowing what these provide, and that LLM serving needs the specialized ones, is practical deployment knowledge.
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ML Infrastructure & GPUsWhat do model-serving frameworks (Triton, TorchServe, vLLM, TGI) provide, and how do you choose?→ML Infrastructure & GPUsServe a 70B-parameter model with high throughput. Do the memory math and name the optimizations.→ML Infrastructure & GPUsHow do you choose an inference-serving stack (vLLM, TGI, Triton, TorchServe) and configure it for throughput?→ML Infrastructure & GPUsHow does continuous (in-flight) batching improve LLM serving throughput vs static batching?→LLM & GenAI FundamentalsWhat is PagedAttention, and why did it transform LLM serving throughput?→ML Infrastructure & GPUsExplain the KV cache: prefill vs decode, why it grows, and how MQA/GQA and PagedAttention help.→
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