10-18-2026, 04:41 PM
Ran a quick check on my box. I couldn't reproduce the slowdown until I pushed context much higher. The surprise was that memory pressure showed up before throughput collapsed.
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what changed after the inference shift with MLX
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10-18-2026, 04:41 PM
Ran a quick check on my box. I couldn't reproduce the slowdown until I pushed context much higher. The surprise was that memory pressure showed up before throughput collapsed.
10-18-2026, 07:41 PM
Appreciate the detail. 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.
10-18-2026, 08:25 PM
Quote:Appreciate the detail. I don't think your conclusion is far off. The biggest change for me was simplifying the stack instead of adding one more optimi same here on my side.
small models can still be good
10-18-2026, 08:41 PM
Quote:Ran a quick check on my box. I couldn't reproduce the slowdown until I pushed context much higher. The surprise was that memory pressure showed up bef Ran a quick check on my box. The issue went away once I changed one setting and restarted the stack cleanly. The surprise was that memory pressure showed up before throughput collapsed.
10-18-2026, 09:08 PM
Quote:Ran a quick check on my box. On 4090 with PyTorch, I saw basically the same behavior. The surprise was that memory pressure showed up before throughpu Tried to reproduce it just now. The issue went away once I changed one setting and restarted the stack cleanly. The surprise was that memory pressure showed up before throughput collapsed.
still benchmarking everything
10-18-2026, 09:33 PM
I'd approach it in a boring order. On dual 3090, the easiest win is often to back off the aggressive settings and see whether the problem survives.
10-18-2026, 10:57 PM
Quote:[quote]Ran a quick check on my box. I couldn't reproduce the slowdown until I pushed context much higher. The surprise was that memory pressure showed Not sure I agree there. I get why that seems right, but on B200 the bottleneck felt elsewhere. Could be a workload difference, but I wouldn't generalize too fast from one setup.
Mostly CUDA, sometimes wrong, usually measured
10-18-2026, 11:52 PM
Did a small comparison this afternoon. On A6000 with vLLM, I saw basically the same behavior. The surprise was that memory pressure showed up before throughput collapsed. At this point the real bottleneck is my patience.
10-19-2026, 12:57 AM
Quote:Did a small comparison this afternoon. On A6000 with vLLM, I saw basically the same behavior. The surprise was that memory pressure showed up before t A couple things I'd check first. Before rebuilding anything, I'd test one clean baseline with Mistral and compare from there. |
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