hip-0251

HIP-251: AI Compute Carbon Footprint. Status Draft. Hanzo's own standard — read this before implementing against it.

HIP-0251: AI Compute Carbon Footprint

Abstract

This HIP establishes the methodology for measuring, calculating, and reporting the carbon footprint of Hanzo AI's training and inference operations. It aligns with the GHG Protocol and provides AI-specific guidance for accurate carbon accounting.

Scope

Operational Boundary

| Activity | Scope | Included | |----------|-------|----------| | Model training | Scope 2/3 | ✅ Yes | | Model inference | Scope 2/3 | ✅ Yes | | Data storage | Scope 2/3 | ✅ Yes | | Development compute | Scope 2/3 | ✅ Yes | | Employee devices | Scope 3 | ✅ Yes | | Cloud services | Scope 3 | ✅ Yes |

Emissions Categorization

| GHG Scope | AI-Relevant Sources | |-----------|---------------------| | Scope 1 | On-site generation (if any) | | Scope 2 | Purchased electricity for owned compute | | Scope 3, Cat 1 | Cloud compute purchases | | Scope 3, Cat 3 | Fuel-related activities | | Scope 3, Cat 11 | Customer inference (if applicable) |

Training Emissions

Training Calculation Methodology

Energy Consumption

Formula:

E_training = Σ(GPU_hours × TDP × PUE) / 1000

Where:

Carbon Emissions

Formula:

CO2e_training = E_training × EF_grid × (1 - R%)

Where:

Reference Values

GPU Power Consumption

| GPU | TDP (W) | Typical Utilization | Effective (W) | |-----|---------|---------------------|---------------| | H100 SXM | 700 | 80% | 560 | | H100 PCIe | 350 | 80% | 280 | | A100 SXM | 400 | 80% | 320 | | A100 PCIe | 300 | 80% | 240 |

Data Center PUE

| Provider/Region | PUE | Source | |-----------------|-----|--------| | Hyperscaler average | 1.1-1.2 | Provider reports | | Colocation average | 1.3-1.5 | Industry benchmarks | | On-premise average | 1.5-2.0 | Industry benchmarks |

Training Tracking

Required Metrics

For each training run: | Metric | Collection Method | |--------|-------------------| | GPU type | Cluster configuration | | GPU hours | Job scheduler logs | | Data center location | Cluster metadata | | Time period | Job timestamps |

Aggregation

| Period | Aggregation | |--------|-------------| | Per run | Individual training job | | Weekly | Development activity | | Monthly | Reporting period | | Annually | Annual report |

Inference Emissions

Inference Calculation Methodology

Per-Request Emissions

Formula:

CO2e_request = E_request × EF_region / 1000

Where:

E_request = (GPU_power × latency_seconds) + memory_energy + network_energy

Estimation Approach

For large-scale inference:

CO2e_inference = Total_GPU_hours × GPU_power × PUE × EF_avg / 1000

Efficiency Metrics

| Metric | Definition | Target | |--------|------------|--------| | CO2e/1K tokens | Emissions per 1,000 tokens | Track and reduce | | CO2e/request | Emissions per API request | Track and reduce | | CO2e/MAU | Emissions per monthly active user | Track and reduce |

Geographic Distribution

Track inference by region: | Region | % of Requests | Grid Factor | Weighted Factor | |--------|---------------|-------------|-----------------| | US-West | X% | 0.35 | Calculated | | US-East | Y% | 0.42 | Calculated | | EU | Z% | 0.28 | Calculated | | Asia | W% | 0.50 | Calculated |

Data & Emission Factors

Grid Emission Factors

| Source | Coverage | Update Frequency | |--------|----------|------------------| | EPA eGRID | US regions | Annual | | EEA | EU countries | Annual | | IEA | Global | Annual | | Provider-specific | Cloud providers | As published |

Cloud Provider Data

| Provider | Data Available | Source | |----------|---------------|--------| | AWS | Region carbon intensity | AWS Customer Carbon Footprint Tool | | GCP | Carbon-free energy % | Google Cloud Carbon Footprint | | Azure | Emissions reporting | Microsoft Sustainability Calculator |

Data Quality

| Level | Definition | Use | |-------|------------|-----| | Primary | Measured data | Preferred | | Secondary | Provider-reported | Acceptable | | Tertiary | Industry average | Gap-filling |

Reporting Standards

Internal Reporting

Monthly Dashboard

| Metric | Display | |--------|---------| | Total compute emissions | CO2e (tonnes) | | Training vs inference split | % breakdown | | YoY change | % change | | Efficiency trend | CO2e/request over time |

Quarterly Report

| Section | Contents | |---------|----------| | Summary | Total emissions, trends | | Training | Major training runs, emissions | | Inference | Volume, efficiency | | Initiatives | Reduction progress |

External Reporting

Annual Disclosure

| Report | Contents | |--------|----------| | ESG Report | Summary metrics, targets | | CDP Response | Detailed methodology | | Model Cards | Per-model training emissions |

Model Card Emissions

For each model release:

training_emissions:
  total_co2e_tonnes: X
  gpu_hours: Y
  energy_kwh: Z
  data_centers: [list]
  renewable_percentage: W%
  methodology: "HIP-251"

Reduction Strategies

Training Efficiency

| Strategy | Impact | Implementation | |----------|--------|----------------| | Efficient architectures | 10-50% reduction | Architecture research | | Mixed precision | 30-50% speedup | Training configuration | | Gradient checkpointing | Memory vs compute | Based on model size | | Curriculum learning | 10-30% reduction | Training methodology |

Inference Efficiency

| Strategy | Impact | Implementation | |----------|--------|----------------| | Model quantization | 2-4x efficiency | INT8/INT4 deployment | | Speculative decoding | 2-3x speedup | Inference optimization | | Batching | Improved utilization | Request aggregation | | Caching | Variable | Response caching |

Infrastructure

| Strategy | Impact | Implementation | |----------|--------|----------------| | Green regions | 30-90% reduction | Region selection | | Renewable PPAs | Up to 100% reduction | Energy procurement | | Efficient hardware | 20-50% per generation | Hardware refresh | | Cooling optimization | PUE improvement | Data center ops |

Targets

Absolute Targets

| Year | Target | Baseline | |------|--------|----------| | 2025 | Establish baseline | Measure all emissions | | 2027 | -30% vs baseline | Reduction | | 2030 | Net zero | Reduction + offsets |

Intensity Targets

| Year | CO2e/1K tokens | CO2e/request | |------|----------------|--------------| | 2025 | Baseline | Baseline | | 2027 | -50% | -50% | | 2030 | -80% | -80% |

Renewable Energy

| Year | Renewable % | |------|-------------| | 2025 | 50% | | 2027 | 80% | | 2030 | 100% |

Verification

Internal Verification

| Activity | Frequency | |----------|-----------| | Data validation | Monthly | | Calculation review | Quarterly | | Methodology audit | Annual |

External Verification

| Activity | Frequency | Standard | |----------|-----------|----------| | Third-party audit | Annual | ISO 14064-3 | | CDP verification | Annual | CDP methodology |

Related HIPs

Changelog

| Version | Date | Changes | |---------|------|---------| | 1.0 | 2025-12-17 | Initial draft |

Copyright

Copyright and related rights waived via CC0.