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prefill bottlenecks on longer prompts on A6000 - Printable Version +- Inference (https://www.datasculpting.ai/inference) +-- Forum: Inference Engines (https://www.datasculpting.ai/inference/forumdisplay.php?fid=9) +--- Forum: Other Engines (https://www.datasculpting.ai/inference/forumdisplay.php?fid=39) +--- Thread: prefill bottlenecks on longer prompts on A6000 (/showthread.php?tid=1455) |
Re: prefill bottlenecks on longer prompts on A6000 - labline - 10-18-2026 Quote:subscribed, curious where this lands. A couple questions before I guess too hard. Do you see the same thing with a smaller batch or shorter prompt? Re: prefill bottlenecks on longer prompts on A6000 - vrambandit - 10-18-2026 Thanks, that helps. That matches what I saw on B200. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable. Re: prefill bottlenecks on longer prompts on A6000 - deltastation - 10-19-2026 Did a small comparison this afternoon. I couldn't reproduce the slowdown until I pushed context much higher. Re: prefill bottlenecks on longer prompts on A6000 - rocmstack25 - 10-19-2026 subscribed, curious where this lands. prefill bottlenecks on longer prompts on A6000 - homelabhenry - 10-19-2026 This keeps coming up in conversation so I figured I'd start a thread. One thing I keep noticing is that people underestimate speculative decoding until they actually try to run this stuff for more than one afternoon. Wondering if this is just where the whole space is heading now. For context, I'm not chasing leaderboard numbers. I mostly want the system to feel consistent day to day. Re: prefill bottlenecks on longer prompts on A6000 - quiettoken - 10-19-2026 My first guess would be this: Before rebuilding anything, I'd test one clean baseline with Mixtral and compare from there. Once you know the plain path works, then add the faster path back in piece by piece. |