# Open Kernel broad paged split-K attention | PTLLM > A custom attention kernel can help decoding while losing verification; compare each dispatch regime with the current incumbent as well as the official… Canonical: https://posttrainllm.com/experiments/openkernel-attention-splitk-broad-20261023 Experiment · runtime perf A custom attention kernel can help decoding while losing verification; compare each dispatch regime with the current incumbent as well as the official baseline. Retained outcome: regressed. **Outcome:** regressed **Family:** runtime perf **Evidence confidence:** exact **Record:** infrastructure ## Evidence readout 2026-10-23 H200 check 34c48a28a66d46d3 passed correctness with no flags: 1.0564x aggregate, reported 95% interval 1.0405-1.0722x, below the selected 1.1357x attention candidate. Full-context verification at batches 16 and 32 measured 0.553x and 0.537x versus SGLang. ## What the attempt taught A custom attention kernel can help decoding while losing verification; compare each dispatch regime with the current incumbent as well as the official baseline. ## Why it stopped or stayed bounded Applying the same split-K kernel to eight-token long-context verification nearly doubled latency; larger sliding verification also lost against the existing fused-preparation candidate. ## Method and scope - Experiment family: runtime perf. - Record kind: infrastructure. ## Disposition Experiment closed after scored run beb82b4224554523 finished at rank 283/749, outside the owner top-100 target. Retain the research artifact; no further jobs. This retained disposition is historical evidence, not authorization to restart the experiment. A new run needs a fresh question, frozen evaluator, explicit resource budget, and scoped tracking issue. ## How to read this dossier This canonical page separates the retained repository decision from the material that informed it. A source may describe an external claim, historical measurement, local observation, or planned procedure; those evidence classes are not interchangeable. Follow the provenance links before reusing a number or method. Status and disposition describe what PostTrainLLM retained when this record was indexed. They are not a live product promise, a newly run benchmark, or permission to restart historical work. Unknown or unmeasured fields remain unknown rather than being treated as zero. To reuse the record, first name the exact claim you need and trace it to the linked source. Then check whether the original environment, model revision, data split, evaluator, hardware, and budget match the proposed use. If they do not, treat the record as a hypothesis or design reference and run the smallest fresh comparison that can falsify it. Preserve negative outcomes and regressions beside any improvement; a local win on one slice does not silently become a general capability claim. ## Source provenance The normalized record comes from `docs/attempts.json`. The links below are the tracked evidence and explanatory sources preserved with the record. - [docs/learn/openkernel.md](https://posttrainllm.com/docs/learn/openkernel) - [experiments/openkernel/results.json](https://github.com/PostTrainLLM/posttrainllm/blob/main/experiments/openkernel/results.json) ## Related evidence - [Primary proof](https://posttrainllm.com/docs/learn/openkernel) - [Applicable recipe](https://posttrainllm.com/recipes) - [Learning path](https://posttrainllm.com/learn/paths/runtime-and-agents) Canonical record: `experiment:openkernel-attention-splitk-broad-20261023`