Experiment · runtime perf

Open Kernel narrow paged split-K attention

Use measured dispatch boundaries to retain custom attention wins and avoid verification regressions; preserve live cache metadata, legal keys and FP32 softmax state. 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 3439c74e04594576 passed correctness with no flags: 1.2804x aggregate, reported 95% interval 1.2612-1.2996x. All 12 benchmark shapes measured faster than SGLang; single-request sliding decoding reached 2.05x.

What the attempt taught

Use measured dispatch boundaries to retain custom attention wins and avoid verification regressions; preserve live cache metadata, legal keys and FP32 softmax state.

Why it stopped or stayed bounded

The custom kernel is selected only for one-token decoding and single-request sliding verification on supported configurations; other shapes retain FA3, and full-layer composition has not been measured.

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.