So I’ve been curious for a while now about AI inference costs and their relation to electricity power cost. Inference will be done by GPUs which are in Servers which are in Data Centers. Data Centers, Servers and GPUs consume power. I wanted to have research presented to me on the relationship of inference cost with electricity cost. I wanted a comparison to be run on electricity cost in various parts of the world and what the inference cost would be in the country.
I tried to get Perplexity to do the research for me. Here is my raw prompt to Perplexity Deep Research:
“
Hi, running AI continuously is an inference operation. continuous inferencing. running a GPU is costly as well in terms of eating up electricity power. please do a research on USD $ cost of a sample AI query such as those done to ChatGPT and their corresponding electricity power cost. Please do a research on electricity cost in Russia, China, Asia, North America, Australia, Europe and give a comparison report on where running AI inference continuously is most cheapest due to GPU power consumption or whole server power consumption or whole data center power consumption
“
Here is the answer:
“
The Global Economics of Continuous AI Inference: Regional Electricity Cost Analysis and Comparative Efficiency
The rapid proliferation of artificial intelligence (AI) systems like ChatGPT has intensified scrutiny of their energy demands and operational costs. Continuous inference—the real-time processing of queries by AI models—requires substantial computational resources, primarily powered by electricity. This report quantifies the electricity costs of AI inference across seven regions (Russia, China, Asia-Pacific excluding China, North America, Australia, and Europe), analyzes GPU/server/data center efficiency factors, and identifies the most cost-effective jurisdictions for AI operations.
Understanding AI Inference Power Consumption
The Energy Footprint of Large Language Models
Modern AI systems like GPT-4o consume between 0.3 watt-hours (Wh) for basic text queries and 40 Wh for complex, context-heavy interactions, depending on input/output token counts and model architecture[3][4]. These figures represent a 10x reduction from earlier estimates due to hardware and algorithmic optimizations, yet they remain orders of magnitude higher than traditional search engines like Google (0.0003 kWh/query)[2][3].
The energy cost stems from three components:
- GPU Computation: High-performance GPUs (e.g., NVIDIA A100/H100) process matrix multiplications at ~400-700 watts per chip.
- Cooling Systems: Data centers expend 30-40% of total energy on cooling[5].
- Ancillary Infrastructure: Networking, storage, and failover systems add ~15-20% overhead[5].
For context, processing 1 million ChatGPT queries with GPT-4o (assuming 0.3 Wh/query) would require 300 kWh—equivalent to powering 10 U.S. households for a day[3][4].
Regional Electricity Cost Breakdown
Industrial Electricity Rates (2025 Q1)
| Region | Avg. Cost ($/kWh) | Key Market Characteristics |
|---|---|---|
| Russia | 0.06 – 0.08 | Subsidized rates for tech sectors; reliance on nuclear/hydrocarbon energy |
| China | 0.08 – 0.12 | State-controlled pricing; coal-dominated grid with expanding renewables in tech hubs |
| Asia-Pacific | 0.10 – 0.18 | Wide variance: India (~$0.10), Singapore (~$0.18), Southeast Asia (~$0.12) |
| North America | 0.12 – 0.16 | U.S. ($0.132 avg), Canada ($0.10); deregulated markets with renewable incentives |
| Australia | 0.14 – 0.20 | High renewable penetration (solar/wind) but transmission costs inflate prices |
| Europe | 0.18 – 0.30 | Carbon taxation in EU; Nordic countries (~$0.18) vs. Germany (~$0.30) |
Sources: U.S. EIA (2024), IEA World Energy Outlook (2025), regional utility reports
Cost per Query Analysis
Using GPT-4o’s 0.3 Wh/query baseline[3], we calculate electricity expenses across regions:
| Region | Cost per Query ($) | Cost per 1M Queries ($) | Annual Cost for 1B Queries ($) |
|---|---|---|---|
| Russia | 0.0000048 – 0.0000064 | 4.80 – 6.40 | 4,800 – 6,400 |
| China | 0.0000064 – 0.0000096 | 6.40 – 9.60 | 6,400 – 9,600 |
| Asia-Pacific | 0.0000080 – 0.0000144 | 8.00 – 14.40 | 8,000 – 14,400 |
| North America | 0.0000096 – 0.0000128 | 9.60 – 12.80 | 9,600 – 12,800 |
| Australia | 0.0000112 – 0.0000160 | 11.20 – 16.00 | 11,200 – 16,000 |
| Europe | 0.0000144 – 0.0000240 | 14.40 – 24.00 | 14,400 – 24,000 |
Assumes 0.3 Wh/query; excludes cooling/ancillary costs
Drivers of Cost Variability
1. Energy Sourcing and Grid Composition
- Russia: Low-cost nuclear (20%) and natural gas (60%) dominate, with industrial subsidies keeping rates at $0.06-$0.08/kWh[4].
- China: Coal provides 65% of electricity, but tech zones like Shenzhen leverage hydro/solar at $0.08-$0.10/kWh for AI data centers[4].
- Europe: High renewable adoption (45% in EU) is offset by carbon taxes, pushing Germany to $0.30/kWh[5].
2. Cooling Efficiency
- Nordic Countries: Free-air cooling reduces data center energy use by 25% vs. global average[5].
- Southeast Asia: Humid climates necessitate energy-intensive liquid cooling (+15% overhead)[5].
3. Government Incentives
- Russia/China: Tax breaks and subsidized land for AI infrastructure[4].
- U.S./Canada: Renewable energy credits cut effective rates by 10-15% in states like Texas[4].
Regional Competitiveness for AI Inference
1. Russia
Advantages:
- Lowest electricity costs ($0.06-$0.08/kWh) due to state subsidies.
- Cold climates in Siberia/Moscow enable free cooling 8 months/year.
Challenges:
- Geopolitical sanctions limit access to advanced GPUs.
- Aging grid infrastructure causes reliability issues.
2. China
Advantages:
- Integrated tech corridors (e.g., Beijing-Shenzhen) with dedicated AI power zones.
- Domestic GPU production (e.g., Huawei Ascend) circumvents export controls.
Challenges:
- Data localization laws complicate international deployments.
- Coal dependency raises long-term sustainability concerns.
3. North America
Advantages:
- High GPU density (NVIDIA/Microsoft partnerships).
- Renewable-rich regions (Texas, Quebec) offer $0.10-$0.12/kWh for PPAs.
Challenges:
- Land and labor costs inflate total expenses by 20-30% vs. Asia[4].
4. Europe
Advantages:
- Strong renewable grids in Scandinavia (86% hydro/wind).
- Carbon-neutral mandates align with ESG goals.
Challenges:
- Highest electricity costs ($0.18-$0.30/kWh) erode competitiveness.
Strategic Recommendations
- Hybrid Deployment: Split workloads between low-cost regions (Russia/China) for batch processing and high-efficiency zones (Nordics/Texas) for latency-sensitive tasks.
- Cooling Optimization: Deploy immersion cooling in tropical regions to cut ancillary energy use by 30%[5].
- Renewable PPAs: Secure 10-year power purchase agreements in Texas/Quebec to lock in rates below $0.10/kWh.
Conclusion
Continuous AI inference remains most economical in Russia and China, where industrial electricity rates fall below $0.10/kHz, coupled with state support for AI infrastructure. However, geopolitical and sustainability risks necessitate diversification. North America and Australia offer balanced cost-performance profiles for enterprises prioritizing stability, while Europe’s high costs limit its role to niche, ESG-aligned deployments. As AI models grow more complex, energy efficiency innovations—not just electricity pricing—will dictate the next phase of global AI competitiveness[3][4].
Citations:
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“
Here is a link to the chat:
https://www.perplexity.ai/search/hi-running-ai-continuously-is-ufywJZlERQq_6qJVH6qQjQ
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My (Habib) comments:
This was an amazing first look experience with Deep Research. It shows that AI can help research various topics and give insightful answers. The internet is HUGE and a tool which searches and regurgitates the vast amount of information in an insightful manner is a serious productivity enhancement. Having said this, it still needs someone knowledgeable in the domain to submit a good query/question and to read and evaluate the answer.
networkfuel 