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Core
Model Serving Frameworks
You seldom build a serving stack from scratch; frameworks take care of the production plumbing. General servers (Triton, TorchServe, KServe) host many model types with dynamic batching, multi-model hosting, and versioning. LLM-specific servers (vLLM, TGI, TensorRT-LLM) add the essentials general servers miss: continuous batching, paged KV cache, and token streaming. AI, ML, and GenAI engineer 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?→ML Infrastructure & GPUsCompare LLM inference engines: vLLM, SGLang, TensorRT-LLM, TGI, and llama.cpp. What actually differs?→LLM & GenAI FundamentalsWhat is PagedAttention, and why did it transform LLM serving throughput?→
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