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Scenarios Zoo

Calculation Contracts and Reference Statistics

Scenarios.* contains reusable calculation settings and named workload/system cases. The scenario domain reference documents their class contracts: inputs, equations, assumptions, exclusions, calculated properties, and examples. The generic Scenario class provides a composed scorecard; calculation scenarios expose their own properties or solver results. Choosing a Python API explains the distinction. Use Classes A–Z when you already know a type name.

Use Hardware.* for devices, Systems.* for composed physical systems, Infrastructure.* for facility and grid facts, and Ops.* for operational policies. A reusable calculation selects these facts and supplies its declared workload assumptions. These categories apply across all textbook volumes.

Reference statistics

The tables below browse ReferenceStats.*, a separate catalog of sourced, non-executable comparison anchors. They are not calculation scenarios. Provenance records where each reference number came from, while its registry path records what kind of fact it is.

Workload scale anchors

Entry Value Description
ChatGPT prompts per day 2500000000.0 Prompts ChatGPT receives from users worldwide each day, July 2025 (OpenAI to Axios).
Gmail emails per day 121000000000.0 Approximate daily Gmail volume.
Google searches per day 13700000000.0 Lower bound from Google’s disclosure of more than 5 trillion searches a year (2025).
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) 0.83 Modeled for the 175B reference job; pipeline fill and drain become visible (~6% of step).
Scaling efficiency (256 GPUs) 0.87 Modeled for the 175B reference job at a fixed GPT-3 batch; tensor-parallel communication is the main loss.
Scaling efficiency (32 GPUs) 0.9 Book convention for small jobs; the 175B reference job needs 64 GPUs per TP8xPP8 replica, so this tier is not modeled.
Scaling efficiency (8192 GPUs) 0.56 Modeled for the 175B reference job at a fixed GPT-3 batch; pipeline bubble dominates (~33% of step). Production runs with larger batches and interleaved pipelines retain more (MegaScale).
Per-accelerator compute efficiency 0.45 Fraction of peak each accelerator achieves on its local compute before communication losses.

Storage training corpus

Entry Value Description
Storage running-example checkpoint bytes per parameter 112.0 Resumable mixed-precision Adam checkpoint footprint: FP16 weights, FP32 master weights, and two FP32 moments; gradients excluded.
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 training step time 200 ms Reference per-step compute time the input pipeline must keep fed.
Storage running-example token ID width 32.0 Serialized token-ID width for the tokenized corpus.
Storage running-example tokenized shard size 24000000000.0 Reference size of one tokenized training shard file.
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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