# Open Kernel small-batch dense split-K and native tiny router | PTLLM > Separate public-shape regimes before composing improvements; more splits add a reduction launch and may lose despite sufficient parallel work. Retained… Canonical: https://posttrainllm.com/experiments/openkernel-dense-splitk-20261024 Experiment · runtime perf Separate public-shape regimes before composing improvements; more splits add a reduction launch and may lose despite sufficient parallel work. Retained outcome: regressed. **Outcome:** regressed **Family:** runtime perf **Evidence confidence:** exact **Record:** infrastructure ## Evidence readout 2026-10-23 H200 check a2d8bfdabc884c1f passed correctness with no flags: 1.0485x aggregate, reported 95% interval 1.0328-1.0643x. This is an off-board check, not a scored submission. ## What the attempt taught Separate public-shape regimes before composing improvements; more splits add a reduction launch and may lose despite sufficient parallel work. ## Why it stopped or stayed bounded Aggregate 1.0485x is below selected 1.0673x. Pooler at 8 and 16 tokens improved about 10 percent over SGLang, but 32-token dense paths and the tiny-router replacement regressed. ## 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-dense-splitk-20261024`