Thread Rating:
  • 0 Vote(s) - 0 Average
  • 1
  • 2
  • 3
  • 4
  • 5
dedicated AI gadgets you actually use for Gemma
#1
Spent some time on this today and wanted to compare notes.

I'm comparing two approaches here: keep it simple on mini PC, or squeeze more out of the stack and deal with the complexity.

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

My use case is pretty ordinary: local chat, some document work, and occasional batch jobs.
Reply
#2
worth checking the driver first.
Reply
#3
A couple questions before I guess too hard. What model, what runtime, and roughly what context size?
Reply
#4
I think the bottleneck is somewhere else. I wouldn't optimize around that first. On my side the bigger swing came from the runtime and not the model. Worth testing the simpler explanation before adopting the more interesting one.
Reply
#5
Not sure I agree there. I wouldn't optimize around that first. On my side the bigger swing came from the runtime and not the model.
learning in public
Reply
#6
Not sure I agree there. I get why that seems right, but on 5090 the bottleneck felt elsewhere. Worth testing the simpler explanation before adopting the more interesting one.
team red, team patient
Reply
#7
I'd approach it in a boring order. If you're on vLLM, I'd confirm the version first and then reduce variables until the behavior is obvious.
Reply
#8
I don't think that's the whole story. The reported numbers can look compelling, but in ordinary usage I didn't see the same benefit.
learning in public
Reply


Forum Jump:


Users browsing this thread: 1 Guest(s)