Methodology for calculating the normalized, amortized emissions from training AI models
Component | Disclosed data |
---|---|
GPU | Nvidia A100 80GB |
Server | HPE Apollo 6500 Gen10 Plus |
Number of GPUs | 384 |
Number of servers | 48 |
Training location | France |
Component | Disclosed data |
---|---|
Total reserved time | 118 days |
Reservation start time | January 2022 (?) |
GPU hours for final model | 1,082,990 |
Missing data point | Mechanism to replace |
---|---|
GPU model | Use the most common GPU for the training year (for instance, 2022 is Nvidia A100) |
Server model | Use the most common server or instance type for the training year |
GPUs used | Use the average cluster size for similar models |
Servers used | Divide GPUs used by average GPUs per server |
Location | Use the US as a relatively high-carbon country |
Datacenter PUE | Use location average |
Datacenter WUE | Use location average |
Total reserved time | Use the average ratio of reserved time to GPU hours |
Reservation start time | Use the published model date minus the total reserved time |
GPU hours for final model | Predict using parameters and architecture per OpenCarbonEval |
GPU hours for intermediate models | Predict based on ratio of final to intermediate for other disclosed models |
Component | Disclosed data |
---|---|
Total reserved time | 289 days (118 x 2.45) |
Reservation start time | August 2021 (finish date of June 2022 minus 289 days) |
GPU hours for final model | 2,653,326 (1,082,990 x 2.45) |
Component | Value |
---|---|
Server embodied emissions | 2500 kgCO2e for similar model |
GPU embodied emissions | 318 kgCO2e |
Usage energy per GPU | 428 W |
Datacenter PUE | 1.1 (Google average) |
Grid intensity | 57 kgCO2e / kWh |
Server use life | 4 years given rapid pace of change in GPU market |
Projected utilization | 95% given intense demand for GPUs |
Component | Value |
---|---|
Datacenter WUE | 1.8 L/kWh (US average) |
Electricity WUE | 3.67 L/kWh - note that 2022 data nuclear data from FR indicates lower numbers that WRI report |
Manufacturing WUE | 412 L/GPU |
Data point | Month 1 | Month 2 | Month 3 | … | Month N |
---|---|---|---|---|---|
Remaining use life | N | N - 1 | N - 2 | 0 | |
Training cost remaining (TCR) | TC | TC - TC / N | TC - 2 x TC / N | 0 | |
Projected inferences remaining (PCR) | PI | PI x (N - 1) / N | PI X (N - 2) / N | 0 | |
Training cost per inference (TPI) | TCR1 / PCR1 | TCR2 / PCR2 | TCR3 / PCR3 | 0 | |
Training cost “billed” (TCB) | TC/N x TPI | TCB1 + TC/N x TPI | TC |
Data point | Month 1 | Month 2 | Month 3 | … | Month N |
---|---|---|---|---|---|
Remaining use life | N | N - 1 | N - 2 | 0 | |
Training cost remaining (TCR) | TC | TC - TCB1 | TC - TCB2 | 0 | |
Projected inferences remaining (PCR) | PI | AI1 x (N - 1) / N | AI1 x (N - 2) / N | 0 | |
Training cost per inference (TPI) | TCR1 / PCR1 | TCR2 / PCR2 | TCR3 / PCR3 | TC / PI | |
Actual inferences | AI1 | ||||
Training cost “billed” (TCB) | AI1 x TPI | TCB1 + AI2 x TPI | TC |
Data point | Month 1 | Month 2 | Month 3 | … | Month 14 |
---|---|---|---|---|---|
Remaining use life | 14 | 13 | 12 | 0 | |
Training cost remaining (TCR) | 46 mt | 43 mt | 39 mt | 0 | |
Projected inferences remaining (PCR) | 98T | 91 | 84 | 0 | |
Training cost per inference (TPI) | .46g/Mq | .46g/Mq | .46g/Mq | 0 | |
Training cost “billed” (TCB) | 3 mt | 3 mt | 3 mt | 0 |
Data point | Month 1 | Month 2 | Month 3 | … | Month 14 |
---|---|---|---|---|---|
Remaining use life | 14 | 13 | 12 | 0 | |
Training cost remaining (TCR) | 46 mt | 41 mt | 38 mt | 0 | |
Projected inferences remaining (PCR) | 98T | 143T | 132T | 0 | |
Training cost per inference (TPI) | .46g/Mq | .28g/Mq | .28g/Mq | 0 | |
Actual inferences | 11T | ||||
Training cost “billed” (TCB) | 5 mt |