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video model setup pain points with TensorRT-LLM - Printable Version +- Inference (https://www.datasculpting.ai/inference) +-- Forum: Evaluation & Testing (https://www.datasculpting.ai/inference/forumdisplay.php?fid=16) +--- Forum: Quality / Reliability Testing (https://www.datasculpting.ai/inference/forumdisplay.php?fid=68) +--- Thread: video model setup pain points with TensorRT-LLM (/showthread.php?tid=1120) |
video model setup pain points with TensorRT-LLM - forgemixer - 10-12-2026 Posting this in case it saves someone else a little time. If you were starting from scratch today for this use case, what would you choose first and what would you ignore? Simple and boring is a valid answer. I care more about stability than squeezing the last possible tokens/sec out of it. The logs were somehow calmer than I was. Re: video model setup pain points with TensorRT-LLM - vramstream - 10-14-2026 What worked for me was pretty simple. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at token cost. Re: video model setup pain points with TensorRT-LLM - batchwire - 10-14-2026 I tested this tonight. On mini PC with Ollama, I saw basically the same behavior. Re: video model setup pain points with TensorRT-LLM - audioforge - 10-14-2026 I tested this tonight. My result was mixed: first run looked bad, second run looked normal. I expected the model choice to matter more than it did. I'm happy to test one more variant if you want a second data point. Re: video model setup pain points with TensorRT-LLM - quiethouse - 10-15-2026 Good point. That matches what I saw on dual 3090. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable. Re: video model setup pain points with TensorRT-LLM - batchsize - 10-15-2026 Quote:Good point. That matches what I saw on dual 3090. I got better results once I stopped chasing one metric and just looked at whether the workflow felt My first guess would be this: Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at HBM constraints. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it. I'm happy to test one more variant if you want a second data point. Re: video model setup pain points with TensorRT-LLM - llmtrack - 10-15-2026 Ran a quick check on my box. I couldn't reproduce the slowdown until I pushed context much higher. I expected the model choice to matter more than it did. Re: video model setup pain points with TensorRT-LLM - benchplease - 10-15-2026 I tested this tonight. I couldn't reproduce the slowdown until I pushed context much higher. |