+- 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 (/showthread.php?tid=866)
structured output failures - vramarch - 10-08-2026
Thought I'd post a small recipe because a lot of threads ask similar things.
Just sharing a setup that ended up working reasonably well: Mixtral, PyTorch, GB200, with a lot of attention on KV cache pressure.
Maybe useful as a starting point for newer folks.
This is mostly for local work rather than public serving.
Re: structured output failures - latentpeak22 - 10-09-2026
I'd approach it in a boring order. If you're on LM Studio, I'd confirm the version first and then reduce variables until the behavior is obvious.
Re: structured output failures - rasterhall - 10-09-2026
I'd approach it in a boring order. Before rebuilding anything, I'd test one clean baseline with Llama and compare from there. Not glamorous advice, but repeatable baselines save a lot of time.
Re: structured output failures - mintstation99 - 10-12-2026
A couple questions before I guess too hard. Are you testing this on B200 or something else?
Re: structured output failures - flintriver50 - 10-12-2026
A couple things I'd check first. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at structured output.
Re: structured output failures - labpath - 10-13-2026
Appreciate the detail. I ended up in about the same place using TensorRT-LLM.
Re: structured output failures - cachelab - 10-13-2026
A couple things I'd check first. Before rebuilding anything, I'd test one clean baseline with DeepSeek and compare from there.
Re: structured output failures - fusedhall56 - 10-13-2026
A couple things I'd check first. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at quant drift. Once you know the plain path works, then add the faster path back in piece by piece.