# Open Kernel single-token NVFP4 bit decoding and vector accumulation | PTLLM > Bit decoding and one final reduction per output improve the earlier direct GEMV's 271.789-microsecond result, but still lose to Marlin. Unchanged… Canonical: https://posttrainllm.com/experiments/openkernel-moe-direct-fast-20261024 Experiment · runtime perf Bit decoding and one final reduction per output improve the earlier direct GEMV's 271.789-microsecond result, but still lose to Marlin. Unchanged multi-token fallback timings do not establish a new optimization. Spills, occupancy and instruction costs remain unmeasured. Retained outcome: regressed. **Outcome:** regressed **Family:** runtime perf **Evidence confidence:** exact **Record:** infrastructure ## Evidence readout 2026-10-23 H200 check ea95ebe0676248be passed all five fresh correctness cases with no flags: 1.0400x aggregate, reported 95% interval 1.0244-1.0556x. This is an off-board check, not a scored submission. ## What the attempt taught Bit decoding and one final reduction per output improve the earlier direct GEMV's 271.789-microsecond result, but still lose to Marlin. Unchanged multi-token fallback timings do not establish a new optimization. Spills, occupancy and instruction costs remain unmeasured. ## Why it stopped or stayed bounded Aggregate 1.0400x is below selected 1.2236x. The custom one-token path took 137.590 microseconds versus 77.680 for SGLang and the selected source's prior 51.414 measurement. Its two custom GEMVs consume 93.4 percent of diagnostic GPU time. ## 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-moe-direct-fast-20261024`