Target
Reduce memory or improve throughput without invalidating model math.
Failure this recipe addresses
Precision choice causes overflow, drift, excess memory, or slow execution.
Data contract
Deterministic numeric fixtures plus one behavioral smoke.
Method or policy
Compare reference and reduced-precision paths with explicit tolerances before measuring speed.
Evaluation contract
Numerical parity and behavioral parity, then memory and throughput.
- max error
- loss drift
- NaN/Inf
- quality
- hardware fallback
Budget and stop rule
No long loop until single-step parity passes.
Stop at the first unexplained numerical or behavioral failure.
Decision rule
Adopt the lowest precision that passes correctness and produces a real systems win.
Learning exercise
Measure fp32/fp16 error on a tiny matrix operation and choose a tolerance before looking at the result.
Explain why a faster kernel with wrong numerics is not an optimization.
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/recipes/registry.json. The links below are the tracked evidence and explanatory sources preserved with the record.