| { |
| "type": "aggregate_saved_runtime_measurements", |
| "comparison": "Different hardware, cohorts, windows and timing units; not a matched speed ranking or speedup measurement.", |
| "quantiles": "NumPy median and linear-interpolated 0.95 quantile", |
| "measurements": [ |
| { |
| "model": "TextCortex/laya-cybersec", |
| "checkpoint": "R2a", |
| "subset": "single_window", |
| "hardware": "Apple MPS (local Mac GPU)", |
| "unit": "complete single-window input", |
| "scope": "All 279 one-window inputs from the saved 1042-input run; sequential batch-one Laya API scoring, including encoding; no dedicated all-shape warmup. Excludes model loading and PDF extraction.", |
| "source_sha256": "048522ef775ca1402dd37314cb3bb06800ec8e38829d118ab477d7b564b44f9b", |
| "observations": 279, |
| "p50_ms": 95.3577909967862, |
| "p95_ms": 148.5410204855725 |
| }, |
| { |
| "model": "TextCortex/laya-cybersec", |
| "checkpoint": "R2a", |
| "subset": "all_inputs", |
| "hardware": "Apple MPS (local Mac GPU)", |
| "unit": "complete saved input, including all windows", |
| "scope": "All 1042 saved inputs: 723 clean PDF texts, 107 attack excerpts and 212 skills; 1500-character windows with 200-character overlap, scored sequentially. Excludes model loading and PDF extraction.", |
| "source_sha256": "048522ef775ca1402dd37314cb3bb06800ec8e38829d118ab477d7b564b44f9b", |
| "observations": 1042, |
| "p50_ms": 730.2949790027924, |
| "p95_ms": 4570.080316357779 |
| }, |
| { |
| "model": "jev-latest", |
| "subset": "pdf_chunk_requests", |
| "hardware": "Hosted API", |
| "unit": "successful chunk request", |
| "scope": "10533 successful requests for 830 saved PDF inputs; includes network and provider time, excludes prior failed attempts and retry backoff. Concurrent request harness; not complete-document wall time. Provider revision unavailable.", |
| "source_sha256": "4ecff2adb4f4bf57aff4cffd8fa316144d1d974d3cbe00ac2584336ca3bb4dcc", |
| "observations": 10533, |
| "p50_ms": 263.58416699804366, |
| "p95_ms": 348.8895498017257 |
| }, |
| { |
| "model": "TextCortex/clef-cybersecurity", |
| "checkpoint": "validation-selected epoch 3, calibrated", |
| "subset": "length_rank_sample", |
| "hardware": "NVIDIA B200", |
| "unit": "complete saved input, including all windows", |
| "scope": "Complete saved text tokenization and model scoring; excludes loading, PDF extraction, network and queue time. All measured shapes warmed; batch one, accelerated FLA/causal-conv1d runtime. 64 uniformly spaced ranks by character length; independent of labels and scores", |
| "documents": 64, |
| "repetitions": 2, |
| "peak_allocated_gib": 19.843493461608887, |
| "source_sha256": "ced52ccc1ad8ef9c5a6342fa70adf37d3d28bb56151e8054f1125105355d19a8", |
| "observations": 128, |
| "p50_ms": 40.00461706891656, |
| "p95_ms": 510.47315176110715 |
| } |
| ], |
| "privacy": "Aggregate values only; no customer text, identifiers or individual measurements." |
| } |
|
|
