AlphaGenome distilled model time per variant
Inference hardware of AlphaGenome
The distilled student model makes predictions for all modalities and cell types with a single device call per variant, taking less than 1 s on an NVIDIA H100 GPU.
Descriptive fact transcribed from the pinned source.
Evidence
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Evidence table
Inspect claims, sources and review details
Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
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7 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Claimed field inference_hardware Context-only references | Advancing regulatory variant effect prediction with AlphaGenome (Nature), Europe PMC full-text XML Results, 'Unifying DNA sequence-to-function model', paragraph 2: 'Taking less than 1 s on an NVIDIA H100 GPU, the student model is highly efficient for large-scale variant effect prediction relative to the alternative approach of ensembling several independently trained models.' Version: Nature, version of record 28 January 2026; PMC12851941 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Limitations1 values
| Advancing regulatory variant effect prediction with AlphaGenome (Nature), Europe PMC full-text XML Results, 'Unifying DNA sequence-to-function model', paragraph 2: 'Taking less than 1 s on an NVIDIA H100 GPU, the student model is highly efficient for large-scale variant effect prediction relative to the alternative approach of ensembling several independently trained models.' Version: Nature, version of record 28 January 2026; PMC12851941 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Source location Results, 'Unifying DNA sequence-to-function model', paragraph 2: 'Taking less than 1 s on an NVIDIA H100 GPU, the student model is highly efficient for large-scale variant effect prediction relative to the alternative approach of ensembling several independently trained models.' Context-only references | Advancing regulatory variant effect prediction with AlphaGenome (Nature), Europe PMC full-text XML Results, 'Unifying DNA sequence-to-function model', paragraph 2: 'Taking less than 1 s on an NVIDIA H100 GPU, the student model is highly efficient for large-scale variant effect prediction relative to the alternative approach of ensembling several independently trained models.' Version: Nature, version of record 28 January 2026; PMC12851941 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Claimed value The distilled student model makes predictions for all modalities and cell types with a single device call per variant, taking less than 1 s on an NVIDIA H100 GPU. Context-only references | Advancing regulatory variant effect prediction with AlphaGenome (Nature), Europe PMC full-text XML Results, 'Unifying DNA sequence-to-function model', paragraph 2: 'Taking less than 1 s on an NVIDIA H100 GPU, the student model is highly efficient for large-scale variant effect prediction relative to the alternative approach of ensembling several independently trained models.' Version: Nature, version of record 28 January 2026; PMC12851941 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Description Descriptive fact transcribed from the pinned source. Context-only references | Advancing regulatory variant effect prediction with AlphaGenome (Nature), Europe PMC full-text XML Results, 'Unifying DNA sequence-to-function model', paragraph 2: 'Taking less than 1 s on an NVIDIA H100 GPU, the student model is highly efficient for large-scale variant effect prediction relative to the alternative approach of ensembling several independently trained models.' Version: Nature, version of record 28 January 2026; PMC12851941 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Relationship: subject catalog-model-alphagenome Context-only references | Advancing regulatory variant effect prediction with AlphaGenome (Nature), Europe PMC full-text XML Results, 'Unifying DNA sequence-to-function model', paragraph 2: 'Taking less than 1 s on an NVIDIA H100 GPU, the student model is highly efficient for large-scale variant effect prediction relative to the alternative approach of ensembling several independently trained models.' Version: Nature, version of record 28 January 2026; PMC12851941 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Name AlphaGenome distilled model time per variant Context-only references | Advancing regulatory variant effect prediction with AlphaGenome (Nature), Europe PMC full-text XML Results, 'Unifying DNA sequence-to-function model', paragraph 2: 'Taking less than 1 s on an NVIDIA H100 GPU, the student model is highly efficient for large-scale variant effect prediction relative to the alternative approach of ensembling several independently trained models.' Version: Nature, version of record 28 January 2026; PMC12851941 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
Release 2026-11-02-a7f03c9e55a5 · Record review: source checked
1 source record and release history
- Advancing regulatory variant effect prediction with AlphaGenome (Nature), Europe PMC full-text XML · Original source · Nature, version of record 28 January 2026; PMC12851941 full-text XML
Technical metadata and extraction receipts
Stable ID: gfm-guide-20261014-claim-alphagenome-variant-scoring-time
- field
- inference_hardware
- value
- The distilled student model makes predictions for all modalities and cell types with a single device call per variant, taking less than 1 s on an NVIDIA H100 GPU.
- source locator
- Results, 'Unifying DNA sequence-to-function model', paragraph 2: 'Taking less than 1 s on an NVIDIA H100 GPU, the student model is highly efficient for large-scale variant effect prediction relative to the alternative approach of ensembling several independently trained models.'
- review
- method: source-hash-verification; ai-assisted-source-review; method note: Each source sentence the claim rests on asserted verbatim in the reviewer's own text extraction (review/check_gfm_guide.py); the claim text was then read against them.; reviewer: claude; reviewer note: Independent Claude (Opus 5.5) review agent for PR #150; did not extract this batch; no human review claimed; date: 2026-10-14; artifact sha256: 50d10530afd474d85702813bc9c9e5431406515e44f3425d64603fdb647fee13; retrieval url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12851941/fullTextXML; note: Claim matches the source.
- limitations
- Upper bound stated in prose; batch size, sequence length and whether model loading is included are not stated.
Related records
- subject: AlphaGenome