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A quick note about forum organization with TensorRT-LLM - Printable Version +- Inference (https://www.datasculpting.ai/inference) +-- Forum: Announcements (https://www.datasculpting.ai/inference/forumdisplay.php?fid=5) +--- Forum: Forum Announcements (https://www.datasculpting.ai/inference/forumdisplay.php?fid=6) +--- Thread: A quick note about forum organization with TensorRT-LLM (/showthread.php?tid=810) Pages:
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A quick note about forum organization with TensorRT-LLM - Data Sculpting - 10-06-2026 Small forum update from the Data Sculpting side. We're still tuning the structure and getting everything in place. Feedback is welcome, especially if a topic feels like it has no obvious home yet. Re: A quick note about forum organization with TensorRT-LLM - Data Sculpting - 10-06-2026 What exact setup are you running? Do you see the same thing with a smaller batch or shorter prompt? Re: A quick note about forum organization with TensorRT-LLM - cozykite - 10-07-2026 What worked for me was pretty simple. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at speculative decoding. 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. Re: A quick note about forum organization with TensorRT-LLM - Data Sculpting - 10-08-2026 Ran a quick check on my box. On 4090 with LM Studio, I saw basically the same behavior. The surprise was that memory pressure showed up before throughput collapsed. Re: A quick note about forum organization with TensorRT-LLM - Data Sculpting - 10-08-2026 worth checking the driver first. Re: A quick note about forum organization with TensorRT-LLM - Data Sculpting - 10-08-2026 that fixed it for me too. Re: A quick note about forum organization with TensorRT-LLM - dockworks23 - 10-08-2026 What worked for me was pretty simple. If you're on PyTorch, 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. Re: A quick note about forum organization with TensorRT-LLM - Data Sculpting - 10-09-2026 My first guess would be this: On GB200, the easiest win is often to back off the aggressive settings and see whether the problem survives. Once you know the plain path works, then add the faster path back in piece by piece. Re: A quick note about forum organization with TensorRT-LLM - quantsupply - 10-09-2026 I tested this tonight. On dual 3090 with PyTorch, I saw basically the same behavior. Re: A quick note about forum organization with TensorRT-LLM - Data Sculpting - 10-10-2026 A couple things I'd check first. If you're on SGLang, I'd confirm the version first and then reduce variables until the behavior is obvious. Once you know the plain path works, then add the faster path back in piece by piece. |