Thread Rating:
  • 0 Vote(s) - 0 Average
  • 1
  • 2
  • 3
  • 4
  • 5
model fallback rules that work
#1
This keeps coming up in conversation so I figured I'd start a thread.

The more I work with local setups, the more it seems like consumer GPUs decides everything downstream.

Curious whether that lines up with what other people are seeing.
learning in public
Reply
#2
A couple questions before I guess too hard. Do you see the same thing with a smaller batch or shorter prompt?
Mostly CUDA, sometimes wrong, usually measured
Reply
#3
Good point. That matches what I saw on GB200.
Reply
#4
Maybe, but I had the opposite result. The reported numbers can look compelling, but in ordinary usage I didn't see the same benefit.
Reply
#5
I'd approach it in a boring order. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at tool calling.
Reply
#6
A couple things I'd check first. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at KV cache pressure.
curious and caffeinated
Reply
#7
Not sure I agree there. I get why that seems right, but on Threadripper box the bottleneck felt elsewhere.
learning in public
Reply
#8
Did a small comparison this afternoon. I couldn't reproduce the slowdown until I pushed context much higher.
learning in public
Reply
#9
Good point. I ended up in about the same place using TensorRT-LLM.
Reply
#10
Quote:Good point. I ended up in about the same place using TensorRT-LLM.

Maybe, but I had the opposite result. I get why that seems right, but on 7900 XTX the bottleneck felt elsewhere. Worth testing the simpler explanation before adopting the more interesting one.
throughput matters, but tail latency matters too
Reply


Forum Jump:


Users browsing this thread: 1 Guest(s)