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structured output failures with vLLM - Printable Version +- Inference (https://www.datasculpting.ai/inference) +-- Forum: Prompting & Context Engineering (https://www.datasculpting.ai/inference/forumdisplay.php?fid=15) +--- Forum: Structured Output (https://www.datasculpting.ai/inference/forumdisplay.php?fid=63) +--- Thread: structured output failures with vLLM (/showthread.php?tid=1392) Pages:
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structured output failures with vLLM - southtinker - 10-17-2026 Posting this in case it saves someone else a little time. I'm helping a small team set up a local workflow. We don't need a giant stack, but we do care about tail latency. I'm especially interested in mistakes worth avoiding. I care more about stability than squeezing the last possible tokens/sec out of it. Re: structured output failures with vLLM - batchpeak - 10-17-2026 That matches what I was seeing. That matches what I saw on M3 Max. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable. Re: structured output failures with vLLM - sparrowhall - 10-18-2026 Quote:[quote]Thanks, that helps. The pattern around quant drift has been pretty similar for me. I got better results once I stopped chasing one metric and j Good point. I don't think your conclusion is far off. Re: structured output failures with vLLM - cudapoint - 10-18-2026 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. Not glamorous advice, but repeatable baselines save a lot of time. Re: structured output failures with vLLM - embershed99 - 10-18-2026 Ran a quick check on my box. On 5090 with TensorRT-LLM, I saw basically the same behavior. I expected the model choice to matter more than it did. Re: structured output failures with vLLM - mangovault67 - 10-19-2026 What worked for me was pretty simple. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at continuous batching. Not glamorous advice, but repeatable baselines save a lot of time. Re: structured output failures with vLLM - macrotinker - 10-19-2026 A couple things I'd check first. Before rebuilding anything, I'd test one clean baseline with Mixtral and compare from there. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it. Re: structured output failures with vLLM - turboarch95 - 10-19-2026 Thanks, that helps. The pattern around quant drift has been pretty similar for me. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable. Re: structured output failures with vLLM - batchsize - 10-19-2026 Quote:Thanks, that helps. The pattern around quant drift has been pretty similar for me. I got better results once I stopped chasing one metric and just loo Not sure I agree there. I wouldn't optimize around that first. On my side the bigger swing came from the runtime and not the model. Not saying you're wrong, just that I wouldn't lock that in as the lesson yet. Re: structured output failures with vLLM - vramghost - 10-19-2026 I don't think that's the whole story. I've seen people blame continuous batching when the real issue was much more mundane. Could be a workload difference, but I wouldn't generalize too fast from one setup. |