Thread Rating:
  • 0 Vote(s) - 0 Average
  • 1
  • 2
  • 3
  • 4
  • 5
RAG chunking that actually helped for Llama
#1
Spent some time on this today and wanted to compare notes.

I've gone back and forth between TensorRT-LLM and llama.cpp for Qwen. The interesting difference for me has been speculative decoding.

What would you pick if you had to live with it for a few months?

This is mostly for local work rather than public serving.
Reply
#2
Good point. That matches what I saw on 4090. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.
learning in public
Reply
#3
Appreciate the detail. That matches what I saw on B200. The biggest change for me was simplifying the stack instead of adding one more optimization layer.

There are a few different conversations hiding inside this thread, so being explicit about the goal helps.
Reply


Forum Jump:


Users browsing this thread: 2 Guest(s)