{ "lesson": "28-self-hosted-serving-selection", "title": "Self-Hosted Serving Selection — Matching Engine to Hardware and Scale", "questions": [ { "stage": "pre", "question": "Which engine does the lesson pick as the dev-laptop one-command default?", "options": [ "llama.cpp", "vLLM", "TGI", "Ollama" ], "correct": 3, "explanation": "" }, { "stage": "check", "question": "What 2025 event changes the default away from TGI for new projects?", "options": [ "TGI entered maintenance mode on December 11, 2025 — only bug fixes going forward", "TGI dropped CUDA support", "TGI raised prices", "TGI was acquired by Anthropic" ], "correct": 0, "explanation": "" }, { "stage": "check", "question": "Which hardware constraint forces llama.cpp and excludes vLLM / TRT-LLM?", "options": [ "CPU only (no accelerator)", "AMD MI300X", "NVIDIA Hopper", "Apple M4" ], "correct": 0, "explanation": "" }, { "stage": "check", "question": "Which engine does the lesson position for agentic multi-turn and prefix-heavy workloads thanks to RadixAttention?", "options": [ "SGLang", "Ollama", "TGI", "llama.cpp" ], "correct": 0, "explanation": "" }, { "stage": "post", "question": "What dev-to-prod pipeline does the lesson recommend on the same GGUF or HF weights?", "options": [ "TGI everywhere", "Ollama in dev and Ollama in prod", "Ollama in dev, llama.cpp in staging, vLLM (or SGLang for prefix-heavy) in prod", "Only TRT-LLM, top to bottom" ], "correct": 2, "explanation": "" }, { "stage": "post", "question": "Why is Ollama discouraged for shared production?", "options": [ "It cannot load GGUF", "It only runs on Windows", "It is closed source", "Go HTTP serialization adds overhead, concurrency management is simpler than vLLM, and OpenTelemetry support lags" ], "correct": 3, "explanation": "" } ] }