Posts: 38
Threads: 9
Joined: Sep 2026
Reputation:
0
Has anyone else run into this?
I'm mostly working with DeepSeek in llama.cpp on M3 Max. The part I'm trying to understand is NVFP4.
Not looking for the perfect answer, mostly what worked in real use.
This is mostly for local work rather than public serving.
curious and caffeinated
Posts: 199
Threads: 27
Joined: Sep 2026
Reputation:
0
that fixed it for me too. The logs were somehow calmer than I was.
M3 Max • MLX • local-first
Posts: 63
Threads: 1
Joined: Sep 2026
Reputation:
0
that fixed it for me too.
still benchmarking everything
Posts: 52
Threads: 5
Joined: Aug 2026
Reputation:
0
Tried to reproduce it just now. I couldn't reproduce the slowdown until I pushed context much higher. I expected the model choice to matter more than it did.
trying not to rebuild again
Posts: 199
Threads: 27
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 INT4.
M3 Max • MLX • local-first
Posts: 49
Threads: 6
Joined: Sep 2026
Reputation:
0
Good point. The pattern around INT4 has been pretty similar for me. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.
Posts: 199
Threads: 27
Joined: Sep 2026
Reputation:
0
I think the bottleneck is somewhere else. I've seen people blame token cost when the real issue was much more mundane.
M3 Max • MLX • local-first
Posts: 211
Threads: 21
Joined: Sep 2026
Reputation:
0
Quote:I think the bottleneck is somewhere else. I've seen people blame token cost when the real issue was much more mundane.
Did a small comparison this afternoon. The issue went away once I changed one setting and restarted the stack cleanly. I expected the model choice to matter more than it did.
7900 XTX / ROCm / somehow still optimistic
Posts: 69
Threads: 3
Joined: Sep 2026
Reputation:
0
What worked for me was pretty simple. Before rebuilding anything, I'd test one clean baseline with Mistral and compare from there. Once you know the plain path works, then add the faster path back in piece by piece.
still benchmarking everything