Posts: 62
Threads: 6
Joined: Sep 2026
Reputation:
0
Did a bit of testing and thought I'd post the result here.
After a few runs on M3 Max, I ended up with decent performance, but the setup only really made sense once I adjusted around prefill/decode split.
Happy to post more details if anyone wants them.
Posts: 40
Threads: 2
Joined: Sep 2026
Reputation:
0
That matches what I was seeing. The pattern around KV cache pressure has been pretty similar for me. The biggest change for me was simplifying the stack instead of adding one more optimization layer.
Posts: 49
Threads: 4
Joined: Sep 2026
Reputation:
0
Thanks, that helps. I don't think your conclusion is far off. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.
Posts: 30
Threads: 4
Joined: Sep 2026
Reputation:
0
I hit almost the same issue last night.
learning in public
Posts: 40
Threads: 1
Joined: Sep 2026
Reputation:
0
I tested this tonight. The issue went away once I changed one setting and restarted the stack cleanly.
learning in public
Posts: 184
Threads: 23
Joined: Sep 2026
Reputation:
0
Quote:I tested this tonight. The issue went away once I changed one setting and restarted the stack cleanly.
A couple questions before I guess too hard. Is this local-only or are you routing anything externally?
Mostly CUDA, sometimes wrong, usually measured
Posts: 52
Threads: 4
Joined: Sep 2026
Reputation:
0
I'd approach it in a boring order. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at token cost. Once you know the plain path works, then add the faster path back in piece by piece.
Posts: 20
Threads: 1
Joined: Sep 2026
Reputation:
0
I hit almost the same issue last night.
still benchmarking everything
Posts: 40
Threads: 4
Joined: Sep 2026
Reputation:
0
Need a little more detail here. Do you see the same thing with a smaller batch or shorter prompt?
small models can still be good