A watershed-district office shipped this path: a local Hugging Face prototype, then an optional TensorRT-LLM optimize for a later decoder, then Triton or NIM in front, with LangChain only on the chatbot feature. Which reading of that stack is correct?
Select an answer to reveal the explanation.
Short Explanation
Hugging Face is the prototype. TensorRT-LLM is the optional optimize. Triton or NIM is the serving layer. LangChain is chatbot orchestration only. That is not “use NeMo for everything,” and it is not a notebook in production.
Full Explanation
The associate implementation path is prototype on Hugging Face, optionally optimize a decoder with TensorRT-LLM, serve with Triton or NIM, and use LangChain only where the chatbot needs orchestration. That mapping is the opposite of collapsing the whole story into NeMo. LangChain is not a server, and a notebook is not a serving layer.