Scenarios Zoo

Workload Statistics, Scale Profiles, and Comparison Anchors

The Scenarios Zoo holds reusable narrative and teaching anchors: real-world workload statistics, energy and emissions comparison baselines, mobile power envelopes, phone batteries, clinical-imaging workflow anchors, storage running examples, and distributed-training scale profiles. These are not hardware specs and not literature categories; provenance records where the number came from, while the registry path records what kind of fact it is.

Use Scenarios.* for reusable problem settings that appear across examples. Use Hardware.* for device specifications, Systems.* for composed physical systems such as nodes/racks/clusters/storage paths, Infrastructure.* for facility, pricing, grid, and capacity facts, and Ops.* for operational policies or overhead profiles. A one-off hypothetical knob can stay local in a LEGO cell when it defines the local problem rather than a shared fact.

Workload scale anchors

Entry Value Description
Gmail emails per day 121000000000.0 Approximate daily Gmail volume.
Google searches per day 8500000000.0 Approximate daily Google search volume.
Waymo sensor data rate (high) 19.0 TB/h Upper-bound AV sensor data generation rate.
Waymo sensor data rate (low) 1.0 TB/h Lower-bound AV sensor data generation rate.

Clinical imaging anchors

Entry Value Description
Retinal screening image size 40000000.0 Reference size for one retinal screening photograph in the rural-clinic workflow.

Energy comparison anchors

Entry Value Description
Boiling a cup (~250 mL) of water 100000 J Approximate energy to bring one cup (~250 mL) of water to a boil.
Smartphone full charge 40000 J Approximate energy to fully charge a smartphone battery.
US household annual electricity 10.7 MWh Rounded annual electricity use baseline for one average US household-year.

Emissions comparison anchors

Entry Value Description
Transatlantic round-trip passenger CO₂e 1000.0 One economy passenger, New York to London and return (kg CO₂e).

Training scale profiles

Entry Value Description
Scaling efficiency (1024 GPUs) 0.5 Reference training scenario with significant communication overhead at 1k GPUs.
Scaling efficiency (256 GPUs) 0.7 Reference training scenario where communication begins to reduce scaling efficiency.
Scaling efficiency (32 GPUs) 0.9 Near-linear scaling regime for a reference training scenario.
Scaling efficiency (8192 GPUs) 0.35 Illustrative scaling efficiency at 8192 GPUs for LLM training.

Storage training corpus

Entry Value Description
Storage running-example checkpoint bytes per parameter 80.0 Reference checkpoint footprint per model parameter.
Storage running-example checkpoint interval 10 min Reference checkpoint interval for checkpoint-count examples.
Storage running-example compressed source corpus 24000000000000.0 Compressed source corpus size for the storage running example.
Storage running-example token ID width 32.0 Serialized token-ID width for the tokenized corpus.
Storage running-example tokenized corpus 48000000000000.0 Derived serialized token-ID corpus size for one epoch.
Storage running-example training tokens 1500000000000.0 Reference token count for the 175B-model storage running example.
Storage running-example training window 30 d Reference training-window duration for checkpoint-count examples.

Mobile power envelopes

Entry Value Description
Background adaptation power budget (high) 1000 mW Upper bound for background on-device adaptation power.
Background adaptation power budget (low) 500 mW Lower bound for background on-device adaptation power.
Mobile ML burst power (high) 10 W Upper bound for brief smartphone ML burst processing.
Mobile ML burst power (low) 5 W Lower bound for brief smartphone ML burst processing.
Mobile ML sustained power (high) 3 W Upper bound for sustained smartphone ML processing.
Mobile ML sustained power (low) 2 W Lower bound for sustained smartphone ML processing.
Mobile NPU peak power 4 W Higher-bound mobile inference power envelope.
Mobile NPU typical power 3 W Typical sustained power for on-device mobile inference.
Object-detector power 2 W Reference power draw of an always-on object-detection workload.

Phone battery anchors

Entry Value Description
Phone battery capacity 3000 mAh Typical flagship smartphone battery charge capacity.
Phone battery energy (capacity x voltage) 39960.0 J Battery energy derived from capacity x voltage.
Flagship phone battery energy 15 h·W Modern flagship smartphone battery pack energy rating.
Phone battery voltage 3.7 V Nominal Li-ion cell voltage.

Python Access

import mlsysim

eff_1024 = mlsysim.ReferenceStats.TrainingScaleProfiles.Eff1024Gpu
retinal_photo = mlsysim.ReferenceStats.ClinicalImaging.RetinalPhotoSize
flight_kg = mlsysim.ReferenceStats.EmissionsAnchors.TransatlanticRoundTripCo2Kg
household_year = mlsysim.ReferenceStats.EnergyAnchors.USHouseholdAnnualElectricity
corpus_tokens = mlsysim.ReferenceStats.StorageTrainingCorpus.TrainingTokens
checkpoint_interval = mlsysim.ReferenceStats.StorageTrainingCorpus.CheckpointInterval
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