Experiment · runtime perf

Cold-start bundle

mmap and direct byte construction are good scaling levers, but large-model cold-start claims need large-model measurements. Retained outcome: worked with caveat.

Outcome
worked with caveat
Family
runtime perf
Evidence confidence
exact
Record
infrastructure

Evidence readout

mmap saved `10-20 ms` and `25 MB` peak RSS on `demo.tinygpt`; lazy embedding deferred `262.1 KB`; package tests passed `43/43`

What the attempt taught

mmap and direct byte construction are good scaling levers, but large-model cold-start claims need large-model measurements.

Why it stopped or stayed bounded

The cold-start path improved the measured demo case, but the 250 MB target file was missing, async spinner overhead added about `30 ms`, and persistent Metal pipeline caching was infeasible through public MLX-Swift APIs.

Method and scope

  • Experiment family: runtime perf.
  • Record kind: infrastructure.

Disposition

Re-measure on a real 50-250 MB checkpoint and keep persistent Metal cache work parked unless MLX-Swift exposes pipeline descriptors.

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

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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.

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Source provenance

The normalized record comes from docs/attempts.json. The links below are the tracked evidence and explanatory sources preserved with the record.