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Evaluate GPU Hosting Emissions for LLM Deployments

Compare location- and market-based emissions, facility power overhead, regional placement, workload records, and provider disclosures for LLM deployments.

Evaluating GPU hosting emissions requires separating electricity accounting, facility overhead, provider allocation, and workload activity. For llm hosting, compare location-based and market-based results without merging them, then confirm that every figure covers the same Region, service, period, and energy boundary. The result should be an auditable record rather than a single provider score.

Set the accounting boundary first

The GHG Protocol Scope 2 Guidance defines Scope 2 as indirect emissions from generating purchased or acquired electricity, steam, heat, or cooling. It uses average grid intensity for the location-based method, derives market-based factors from contractual instruments and the consumer’s choice of electricity supplier or product, and requires companies operating in markets with contractual instruments to report both methods with each result labelled.

For rented infrastructure, keep the provider total, service allocation, electricity consumption, emission factor, and workload record as separate fields. Check whether a provider figure includes shared computing, storage, networking, cooling, lighting, or other facility load before assigning it to an LLM workload. Do not average location-based and market-based results or substitute one for the other: they answer different questions and must remain distinguishable in the final record.

Turn the disclosures into a calculation sheet

Build separate calculations for the two methods. A location-based worksheet can represent the relationship between energy use and grid intensity as follows:

LOCATION_SPECIFIC_ENERGY = YOUR_VALUE
GRID_INTENSITY_FACTOR = YOUR_VALUE
HOURLY_EMISSIONS = LOCATION_SPECIFIC_ENERGY * GRID_INTENSITY_FACTOR
LOCATION_BASED_TOTAL = SUM(HOURLY_EMISSIONS)

For the market-based method, retain the factor associated with the relevant contractual instrument rather than reusing the grid-average factor:

MARKET_SPECIFIC_ENERGY = YOUR_VALUE
CONTRACTUAL_EMISSION_FACTOR = YOUR_VALUE
MARKET_BASED_TOTAL = SUM(MARKET_SPECIFIC_ENERGY * CONTRACTUAL_EMISSION_FACTOR)

Use energy and factor units that can be combined without an undisclosed conversion. Preserve provider-reported units alongside any normalized working fields. If either energy or factor is missing, leave that result unresolved instead of filling it with a model name, GPU capacity, or assumed workload profile.

Each calculation row should contain:

  • Service, resource, and workload identifiers.
  • Region or data-center location, when disclosed.
  • Reporting period and publication date.
  • Energy quantity and its unit.
  • Location-based grid factor.
  • Market-based contractual instrument and factor.
  • Allocation rules for dynamic and idle power.
  • Facility overhead and PUE boundary.
  • Provider methodology version and any recalculation notice.

Keep power efficiency separate from carbon

Power usage efficiency describes facility overhead, not emissions. Google’s PUE explanation describes the ratio as overhead energy relative to the energy used by IT equipment, including cooling and power distribution. It gives PUE 2.0 as an example where each watt of IT power requires an additional watt of overhead, while a value closer to 1.0 means nearly all energy is used for computing.

Represent that relationship without inventing a facility value:

IT_EQUIPMENT_ENERGY = YOUR_VALUE
OVERHEAD_ENERGY = YOUR_VALUE
PUE = (IT_EQUIPMENT_ENERGY + OVERHEAD_ENERGY) / IT_EQUIPMENT_ENERGY

A PUE value still needs an electricity boundary and an emissions factor before it becomes a carbon result. Likewise, a GPU’s memory capacity or advertised power figure does not establish the energy consumed by a particular inference run. Record actual workload timing and keep hardware specifications separate from measured or provider-allocated energy.

When comparing facilities, confirm whether PUE covers the whole facility, an individual hall, a dedicated system, or only a partial boundary. An apparently efficient ratio can still describe a different energy boundary from the provider emissions report.

Keep llm hosting region choices separate from carbon results

AWS’s Regions and Zones guidance advises selecting a Region close to the intended customers. It also says Regions are isolated from each other and that resources are not replicated across Regions automatically.

Record the selected Region before collecting energy and emissions data. Moving an endpoint to reduce user distance can change the location-based calculation because that method follows the grid where consumption occurs, but proximity alone does not prove that emissions are lower. Compare the same workload under the same accounting method, factor source, allocation rule, and reporting period.

Keep replicas in separate records when they operate in different locations. Do not average their results unless your reporting policy explicitly defines that calculation. A latency-driven architecture and an emissions comparison may therefore require different placement decisions.

Schedule workloads only after telemetry is in place

Flexible jobs should carry their start time, stop time, Region, GPU model, device count, host resource use, and deployment version. These fields let you match operational records to provider reporting periods without pretending that request activity is an energy meter.

Google’s Carbon Footprint reporting methodology separates dynamic power used while workloads run from idle power. It allocates idle power using resource allocation across CPU, RAM, SSD, and HDD, while hourly dynamic power is allocated using relative CPU usage. For an LLM deployment, do not relabel that allocation method as direct GPU-energy measurement unless the documentation explicitly makes that connection.

The Prometheus getting-started guide documents a global scrape interval of 15s, job-level overrides, and targets under scrape_configs. A configuration based on those documented keys is:

global:
  scrape_interval: 15s

scrape_configs:
  - job_name: 'YOUR_VALUE'
    static_configs:
      - targets: ['YOUR_VALUE']

Replace the placeholders only with a job name and target endpoint documented by the exporter you operate. Start Prometheus with the documented command:

./prometheus --config.file=prometheus.yml

The same guide places its database in ./data by default through --storage.tsdb.path. Use those operational records to establish when the deployment ran and which target was observed. Keep them distinct from provider energy totals, grid factors, and contractual instruments; an active scrape does not validate an emissions calculation.

Read provider disclosures at their documented grain

The AWS Sustainability key concepts states that AWS reports carbon in metric tons of CO2-equivalent, or MTCO2e; publishes new data monthly by the 21st after usage; and provides history from January 2022. It displays carbon at 1-gram CO2e resolution, with monthly emissions below 0.5 g CO2e appearing as 0. The console breaks out Amazon EC2, Amazon S3, and Amazon CloudFront while displaying all other products as Other, and reports global services such as Amazon CloudFront under Global.

For an LLM deployment, check service-level granularity before comparing totals. If a product is grouped under Other, or a monthly report cannot isolate a particular workload, do not derive per-run emissions from the unallocated total. Record the limitation and use a separately documented allocation method if one is available.

Also preserve the reporting cadence and displayed resolution. A displayed zero under a stated rounding threshold must not be interpreted as proof of no emissions, and a monthly provider total must not be assigned to an individual job without a defensible allocation rule.

Verify the evaluation before using the result

Before accepting an emissions figure, trace it back through the calculation sheet. PUE captures facility overhead rather than carbon itself. GPU hardware specifications describe possible equipment, but they do not replace workload energy data. Provider totals remain distinct from Scope 2 grid calculations, and location-based and market-based results must retain separate labels and evidence.

The linked documents do not provide a universal cross-provider GPU-emissions score. If the official material does not disclose energy, factor, allocation, Region, or reporting boundary, mark the field as unknown rather than inventing a conversion.

This is general emissions-accounting information, not legal or assurance advice. The steps are compiled from the official documentation linked in the text and should be checked against the current version before use. No commands shown here were executed for this article.

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