Title: Discussion 7 — Multi-Backend Servers vs. Single-Engine Serving
Course: AIINFRA 200: Production Inference Serving & GPU Orchestration > Section: Week 07 — NVIDIA Triton Inference Server and Model Repositories

💬 Discussion 7 — Multi-Backend Servers vs. Single-Engine Serving (20 pts)

This week you saw Triton's multi-backend, multi-model architecture — one server capable of hosting PyTorch, ONNX, TensorRT, and Python-backend models side by side, plus dedicated LLM backends (vLLM and TensorRT-LLM). Compare that approach to a single-engine deployment like running vLLM directly on its own, with no Triton in front of it. In your initial post, address:

- What operational complexity does Triton add (model repository structure, config files, versioning) compared to pointing traffic straight at a single-engine server?
- What serving flexibility do you gain in exchange — for a team that needs to support several frameworks, or several model types, at once?
- Describe a scenario from your own work or coursework where you would pick Triton over a single-engine approach, and one where you'd pick the reverse. Be specific about the trade-off that drove your choice.

Respond to at least two classmates, engaging with their reasoning — agree, push back, or add a scenario they didn't consider.