# Open Kernel single-token dense GEMV | PostTrainLLM > A single streaming GEMV can remove split-reduction overhead for one-token projections while retaining existing dispatch for other shapes. Retained outcome:… Canonical: https://posttrainllm.com/experiments/openkernel-dense-gemv-20261023 Experiment · runtime perf A single streaming GEMV can remove split-reduction overhead for one-token projections while retaining existing dispatch for other shapes. Retained outcome: worked with caveat. **Outcome:** worked with caveat **Family:** runtime perf **Evidence confidence:** exact **Record:** infrastructure ## Evidence readout 2026-10-23 H200 scored run 1095cf9b43bf49a0 passed with no flags: 1.0673x aggregate, reported 95% interval 1.0513-1.0834x. All four targeted one-token projections measured 1.13-1.20x versus SGLang, repeating the preceding check gains. ## What the attempt taught A single streaming GEMV can remove split-reduction overhead for one-token projections while retaining existing dispatch for other shapes. ## Why it stopped or stayed bounded The aggregate interval overlaps the previous dense candidate; targeted projection gains do not establish the exact aggregate improvement or gains outside one-token dispatch. ## 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-gemv-20261023`