10-11-2026, 06:19 PM
I think the bottleneck is somewhere else. I get why that seems right, but on 5090 the bottleneck felt elsewhere. Could be a workload difference, but I wouldn't generalize too fast from one setup.
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AWQ vs GPTQ vs GGUF right now
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10-11-2026, 06:19 PM
I think the bottleneck is somewhere else. I get why that seems right, but on 5090 the bottleneck felt elsewhere. Could be a workload difference, but I wouldn't generalize too fast from one setup.
10-12-2026, 02:50 AM
Quote:I think the bottleneck is somewhere else. I get why that seems right, but on 5090 the bottleneck felt elsewhere. Could be a workload difference, but I My first guess would be this: If you're on Ollama, I'd confirm the version first and then reduce variables until the behavior is obvious. Not glamorous advice, but repeatable baselines save a lot of time. There are a few different conversations hiding inside this thread, so being explicit about the goal helps.
learning in public
10-12-2026, 10:52 AM
I hit almost the same issue last night.
2x3090 • llama.cpp • too much VRAM is still not enough
10-12-2026, 11:34 PM
Thanks, that helps. I don't think your conclusion is far off. The biggest change for me was simplifying the stack instead of adding one more optimization layer.
10-13-2026, 04:22 AM
My first guess would be this: Before rebuilding anything, I'd test one clean baseline with Phi and compare from there.
trying not to rebuild again
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