task
stringclasses 1
value | org
stringclasses 7
values | model
stringclasses 9
values | hardware
stringclasses 2
values | date
stringclasses 9
values | prefill
dict | decode
dict | preprocess
dict |
|---|---|---|---|---|---|---|---|
text_generation | NousResearch | Hermes-3-Llama-3.1-8B | a100-large | 2024-10-30-23-07-10 | {
"efficency": {
"unit": "tokens/kWh",
"value": 7896383.83303204
},
"energy": {
"cpu": 0.010771365165906027,
"gpu": 0.03499995191660332,
"ram": 0.000042438637758223256,
"total": 0.04581375572026757,
"unit": "kWh"
}
} | {
"efficiency": {
"unit": "tokens/kWh",
"value": 383715.9018998692
},
"energy": {
"cpu": 0.006909195393007252,
"gpu": 0.016518416020279855,
"ram": 0.000027241396927033754,
"total": 0.023454852810214137,
"unit": "kWh"
}
} | {
"efficiency": {
"unit": "samples/kWh",
"value": 19314595.743536726
},
"energy": {
"cpu": 0.00003136402598271767,
"gpu": 0.000020315571802598242,
"ram": 9.47190721638247e-8,
"total": 0.000051774316857479744,
"unit": "kWh"
}
} |
text_generation | meta-llama | Llama-3.1-8B-Instruct | a100-large | 2024-10-30-19-38-09 | {
"efficency": {
"unit": "tokens/kWh",
"value": 7875937.087230494
},
"energy": {
"cpu": 0.010796036717298752,
"gpu": 0.035094443769978056,
"ram": 0.00004221247271874213,
"total": 0.04593269295999555,
"unit": "kWh"
}
} | {
"efficiency": {
"unit": "tokens/kWh",
"value": 373534.5783406723
},
"energy": {
"cpu": 0.006907515534659228,
"gpu": 0.01715961075545351,
"ram": 0.000027028633857338117,
"total": 0.024094154923970088,
"unit": "kWh"
}
} | {
"efficiency": {
"unit": "samples/kWh",
"value": 18903508.14097379
},
"energy": {
"cpu": 0.000031577332566181814,
"gpu": 0.000021227516981525696,
"ram": 9.538423654320006e-8,
"total": 0.000052900233784250705,
"unit": "kWh"
}
} |
text_generation | EleutherAI | pythia-1.4b | a10g-large | 2024-10-25-14-19-11 | {
"efficency": {
"unit": "tokens/kWh",
"value": 54946841.77704233
},
"energy": {
"cpu": 0.0006950257205501962,
"gpu": 0.004783902188229749,
"ram": 0.000007229079517594969,
"total": 0.00548615698829754,
"unit": "kWh"
}
} | {
"efficiency": {
"unit": "tokens/kWh",
"value": 1185499.0738062232
},
"energy": {
"cpu": 0.001532609044249083,
"gpu": 0.006043184112321255,
"ram": 0.00001594622041976216,
"total": 0.0075917393769901025,
"unit": "kWh"
}
} | {
"efficiency": {
"unit": "samples/kWh",
"value": 33069647.189926323
},
"energy": {
"cpu": 0.000011265942924405358,
"gpu": 0.00001888834844376852,
"ram": 8.491845032760329e-8,
"total": 0.00003023920981850148,
"unit": "kWh"
}
} |
text_generation | microsoft | phi-2 | a10g-large | 2024-10-25-00-12-06 | {
"efficency": {
"unit": "tokens/kWh",
"value": 25532119.335130304
},
"energy": {
"cpu": 0.001485621188480941,
"gpu": 0.010244187250898752,
"ram": 0.000014692518964651205,
"total": 0.011744500958344343,
"unit": "kWh"
}
} | {
"efficiency": {
"unit": "tokens/kWh",
"value": 653974.0928690574
},
"energy": {
"cpu": 0.002664958041045172,
"gpu": 0.011070685912097256,
"ram": 0.000026369096206358968,
"total": 0.013762013049348782,
"unit": "kWh"
}
} | {
"efficiency": {
"unit": "samples/kWh",
"value": 2594586258.0208464
},
"energy": {
"cpu": 3.829965067173665e-7,
"gpu": 0,
"ram": 2.4213983180210174e-9,
"total": 3.8541790503538754e-7,
"unit": "kWh"
}
} |
text_generation | allenai | OLMo-1B-hf | a10g-large | 2024-10-24-18-23-56 | {
"efficency": {
"unit": "tokens/kWh",
"value": 62297846.58810162
},
"energy": {
"cpu": 0.0006078373521613038,
"gpu": 0.004224683213077207,
"ram": 0.0000062823229751878135,
"total": 0.0048388028882136985,
"unit": "kWh"
}
} | {
"efficiency": {
"unit": "tokens/kWh",
"value": 1390874.1413723528
},
"energy": {
"cpu": 0.0012634316170816028,
"gpu": 0.005194258099847594,
"ram": 0.000013061109683948865,
"total": 0.006470750826613145,
"unit": "kWh"
}
} | {
"efficiency": {
"unit": "samples/kWh",
"value": 35562851.946256354
},
"energy": {
"cpu": 0.00001108087887082042,
"gpu": 0.000016956680231938748,
"ram": 8.167052233473494e-8,
"total": 0.000028119229625093902,
"unit": "kWh"
}
} |
text_generation | openai-community | gpt2-large | a10g-large | 2024-10-25-15-05-20 | {
"efficency": {
"unit": "tokens/kWh",
"value": 77907728.2032169
},
"energy": {
"cpu": 0.00042952262899040045,
"gpu": 0.0027924934839930414,
"ram": 0.000004223514289139063,
"total": 0.0032262396272725808,
"unit": "kWh"
}
} | {
"efficiency": {
"unit": "tokens/kWh",
"value": 1343266.704857417
},
"energy": {
"cpu": 0.0016164460666061049,
"gpu": 0.005067730581958951,
"ram": 0.00001590753237599822,
"total": 0.006700084180941058,
"unit": "kWh"
}
} | {
"efficiency": {
"unit": "samples/kWh",
"value": 34837547.66517286
},
"energy": {
"cpu": 0.000010432217698123875,
"gpu": 0.000018202236783837478,
"ram": 7.020663859453463e-8,
"total": 0.00002870466112055589,
"unit": "kWh"
}
} |
text_generation | HuggingFaceTB | SmolLM-135M | a10g-large | 2024-10-23-19-09-15 | {
"efficency": {
"unit": "tokens/kWh",
"value": 218923397.1364946
},
"energy": {
"cpu": 0.00028216661832921095,
"gpu": 0.0011204146741087939,
"ram": 0.000002878352463094162,
"total": 0.001405459644901099,
"unit": "kWh"
}
} | {
"efficiency": {
"unit": "tokens/kWh",
"value": 1478710.1205686298
},
"energy": {
"cpu": 0.0020209151121617142,
"gpu": 0.004044847485875458,
"ram": 0.000020623012510417966,
"total": 0.00608638561054759,
"unit": "kWh"
}
} | {
"efficiency": {
"unit": "samples/kWh",
"value": 35187752.05342098
},
"energy": {
"cpu": 0.000010718486329682895,
"gpu": 0.000017620569652265772,
"ram": 7.992339706452299e-8,
"total": 0.00002841897937901319,
"unit": "kWh"
}
} |
text_generation | HuggingFaceTB | SmolLM-1.7B | a10g-large | 2024-10-24-15-16-02 | {
"efficency": {
"unit": "tokens/kWh",
"value": 41960978.223952904
},
"energy": {
"cpu": 0.0009210179793032473,
"gpu": 0.006402295760721532,
"ram": 0.000009403775706541059,
"total": 0.007332717515731321,
"unit": "kWh"
}
} | {
"efficiency": {
"unit": "tokens/kWh",
"value": 986792.3688060087
},
"energy": {
"cpu": 0.0017958659208215804,
"gpu": 0.007306251844996492,
"ram": 0.000018341901791392123,
"total": 0.009120459667609458,
"unit": "kWh"
}
} | {
"efficiency": {
"unit": "samples/kWh",
"value": 37376068.58504866
},
"energy": {
"cpu": 0.000010438337355784218,
"gpu": 0.000016238901880072376,
"ram": 7.78486547950319e-8,
"total": 0.000026755087890651626,
"unit": "kWh"
}
} |
text_generation | HuggingFaceTB | SmolLM-360M | a10g-large | 2024-10-24-14-22-22 | {
"efficency": {
"unit": "tokens/kWh",
"value": 124481811.63546507
},
"energy": {
"cpu": 0.00038417323045180534,
"gpu": 0.0020836598613710013,
"ram": 0.000003917567631278797,
"total": 0.002471750659454085,
"unit": "kWh"
}
} | {
"efficiency": {
"unit": "tokens/kWh",
"value": 1315130.308155782
},
"energy": {
"cpu": 0.0021575579513180274,
"gpu": 0.004663859981084995,
"ram": 0.00002201039115281833,
"total": 0.0068434283235558405,
"unit": "kWh"
}
} | {
"efficiency": {
"unit": "samples/kWh",
"value": 36616831.97480331
},
"energy": {
"cpu": 0.000010503983233461947,
"gpu": 0.000016727513381886716,
"ram": 7.834823109019483e-8,
"total": 0.00002730984484643886,
"unit": "kWh"
}
} |
Analysis of energy usage for HUGS models
Based on the energy_star branch of optimum-benchmark, and using codecarbon.
Fields
- task: Task the model was benchmarked on.
- org: Organization hosting the model.
- model: The specific model. Model names at HF are usually constructed with {org}/{model}.
- date: The date that the benchmark was run.
- prefill: The esimated energy and efficiency for prefilling.
- decode: The estimated energy and efficiency for decoding.
- preprocess: The estimated energy and efficiency for preprocessing.
Code to Reproduce
As I'm devving, I'm hopping between https://huggingface.co./spaces/AIEnergyScore/benchmark-hugs-models and https://huggingface.co./spaces/meg/CalculateCarbon
From there, python code/make_pretty_dataset.py (included in this repository) takes the raw results and uploads them to the dataset here.
- Downloads last month
- 53