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serving on Blackwell - Printable Version +- Inference (https://www.datasculpting.ai/inference) +-- Forum: Inference Engines (https://www.datasculpting.ai/inference/forumdisplay.php?fid=9) +--- Forum: TensorRT-LLM (https://www.datasculpting.ai/inference/forumdisplay.php?fid=37) +--- Thread: serving on Blackwell (/showthread.php?tid=414) Pages:
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serving on Blackwell - northcraft90 - 09-23-2026 Thought I'd post a small recipe because a lot of threads ask similar things. Maybe useful as a starting point: vLLM on Threadripper box, keeping HBM constraints in mind from the start. If someone wants the exact settings I can clean them up and post them. This is mostly for local work rather than public serving. Re: serving on Blackwell - batchcraft - 09-23-2026 following this because I'm in the same boat. Re: serving on Blackwell - torchtrack24 - 09-23-2026 I'd start with the simpler stack. Re: serving on Blackwell - batchcraft - 09-24-2026 I think the bottleneck is somewhere else. I wouldn't optimize around that first. On my side the bigger swing came from the runtime and not the model. Could be a workload difference, but I wouldn't generalize too fast from one setup. Re: serving on Blackwell - routecrate - 09-24-2026 That matches what I was seeing. I don't think your conclusion is far off. Re: serving on Blackwell - tensorriver - 09-24-2026 What worked for me was pretty simple. On mini PC, the easiest win is often to back off the aggressive settings and see whether the problem survives. Re: serving on Blackwell - northcraft90 - 09-25-2026 What exact setup are you running? Is this local-only or are you routing anything externally? Re: serving on Blackwell - kernelmike - 09-25-2026 following this because I'm in the same boat. Re: serving on Blackwell - quiettoken - 09-26-2026 Thanks, that helps. That matches what I saw on A6000. The biggest change for me was simplifying the stack instead of adding one more optimization layer. Re: serving on Blackwell - kernelmike - 09-26-2026 Quote:Thanks, that helps. That matches what I saw on A6000. The biggest change for me was simplifying the stack instead of adding one more optimization laye Can you fill in a bit more context? Do you see the same thing with a smaller batch or shorter prompt? |