09-14-2026, 10:45 AM
Thanks, that helps. That matches what I saw on Threadripper box. The biggest change for me was simplifying the stack instead of adding one more optimization layer.
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best small quant that still feels smart
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09-14-2026, 10:45 AM
Thanks, that helps. That matches what I saw on Threadripper box. The biggest change for me was simplifying the stack instead of adding one more optimization layer.
09-14-2026, 10:58 AM
What exact setup are you running? Are you testing this on 5090 or something else?
2x3090 • llama.cpp • too much VRAM is still not enough
09-14-2026, 07:27 PM
Thanks, that helps. That matches what I saw on 5090. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.
09-14-2026, 07:56 PM
What exact setup are you running? What model, what runtime, and roughly what context size?
09-15-2026, 08:03 PM
Quote:What exact setup are you running? What model, what runtime, and roughly what context size? I'd approach it in a boring order. Before rebuilding anything, I'd test one clean baseline with Mixtral and compare from there. Not glamorous advice, but repeatable baselines save a lot of time.
2x3090 • llama.cpp • too much VRAM is still not enough
09-15-2026, 08:41 PM
Quote:Quote:What exact setup are you running? What model, what runtime, and roughly what context size? Need a little more detail here. Is this local-only or are you routing anything externally? |
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