The Environmental Impact of Large AI Models: Carbon Footprint and Energy Use

Carbon Emissions from Training Large AI Models

Training state-of-the-art AI models requires immense computational power, leading to significant carbon emissions. For example, OpenAI’s GPT-3 (with 175 billion parameters) is estimated to have consumed about 1,287 MWh of electricity during training, producing roughly 500–550 metric tons of CO₂ (AI's carbon footprint - how does the popularity of artificial intelligence affect the climate? - Plan Be Eco) . For context, that is equivalent to the annual emissions of over 100 gasoline cars or more than 500 transcontinental flight trips (e.g. New York to San Francisco) for a single passenger (The Carbon Emissions of Training AI Models - Voronoi). Newer models are even more resource-intensive. GPT-4’s exact training footprint hasn’t been disclosed (OpenAI hasn’t published its size, though it’s believed to exceed GPT-3’s 175 billion parameters (AI’s Growing Carbon Footprint – State of the Planet), but trends suggest it would be higher. In fact, Meta’s 2024 LLaMA-3 model (~70B parameters) was reported to have almost four times the emissions of GPT-3 , highlighting how rapidly training emissions are rising with each generation.

Not all AI trainings are equally polluting – the energy source and efficiency matter. A notable case study is the BLOOM language model (an open 176B-parameter model) which was trained using mostly carbon-free energy (France’s nuclear-heavy grid). BLOOM’s training emitted only about 50 metric tons of CO₂ – merely a tenth of GPT-3’s footprint – demonstrating that choosing low-carbon power and efficient infrastructure can dramatically cut emissions. A life-cycle analysis found that for BLOOM, nearly half of its 50-ton CO₂ footprint came from the electricity powering GPU chips during training, ~29% from running the data center (cooling, networking, etc.), and about 22% from manufacturing the hardware (The Environmental Impacts of AI -- Primer). This shows that the carbon intensity of the electricity (whether it’s from fossil fuels or renewables) is the dominant factor in AI training emissions .

Energy Consumption and Emissions During AI Inference

After an AI model is trained, it doesn’t stop consuming energy – running the model (inference) for user queries and applications demands ongoing power in data centers. Recent estimates suggest that inference may consume even more total energy than training over a model’s lifetime, especially for popular services . Google engineers observed that about 60% of the energy associated with AI workloads is spent on inference (serving results), versus 40% for the initial training . This is because once deployed, a model like ChatGPT or GPT-4 might serve millions or billions of queries, each requiring computation on power-hungry chips.

Each AI query may seem quick, but the aggregated energy use adds up. By one estimate, a single ChatGPT query can use several times more energy than a standard Google search – on the order of watts of electricity per query, or roughly 4–5 grams of CO₂ emissions for ChatGPT vs about 0.2 grams for a Google search . (Other analyses put ChatGPT’s per-query emissions a bit lower, around 2–3 g CO₂, but still an order of magnitude above a basic web search .) In practical terms, asking 16 questions to an AI chatbot can emit as much carbon as boiling a kettle of water . With ChatGPT reportedly reaching 100 million users per week shortly after launch (AI and energy: Will AI reduce emissions or increase demand? | World Economic Forum), this extra energy demand scales into a substantial carbon footprint. One rough calculation pegged GPT-3’s inference emissions at ~8.4 tons of CO₂ per year under a certain usage level – double the annual carbon output of an average human (The Environmental Impact of ChatGPT | Earth.Org). In other words, keeping these models running for widespread use can produce tons of carbon emissions annually. This has led experts to warn that as AI adoption grows, the “operational” carbon footprint from AI services could eclipse training emissions and continue to rise .

Energy Sources: Renewable vs. Fossil Fuel Power

The source of electricity powering AI computations plays a pivotal role in determining their carbon footprint. Most large AI models run in cloud data centers, and traditionally data centers draw power from the local electrical grid, which in many regions still relies heavily on fossil fuels. Globally, data centers (including those running AI) account for about 2.5%–3.7% of greenhouse gas emissions – more than the entire aviation industry – largely because many are powered by coal- or gas-fired electricity . A recent analysis projected that data centers’ electricity consumption could reach ~1000 TWh by 2026 (roughly the consumption of Japan), double current levels (Reducing AI’s Climate Impact: Everything You Always Wanted to Know but Were Afraid to Ask - BEGIN). If much of that electricity comes from fossil sources, AI could become a major emitter. In essence, an AI model performing the same computations will have a much larger carbon footprint on a coal-fired grid than on a renewable-powered grid.

The good news is that major AI firms and cloud providers are increasingly shifting to renewable energy. According to Microsoft, all big cloud operators have plans to run on 100% carbon-free energy by 2030 . Some are even closer targets – Microsoft itself committed to 100% renewable energy by 2025 for its data centers . Google already claims to match 100% of its data center electricity with renewable purchases (meaning for every MWh it uses, it buys an equivalent MWh from wind, solar, etc.) . In practice this makes Google’s operations net-carbon-neutral, though actual real-time consumption may still draw from the local grid mix. The trend is clear: the AI industry is investing in clean energy through power purchase agreements, on-site solar/wind, and other means to ensure that training and running models can be done with minimal direct emissions.

Crucially, locating AI computations in regions with cleaner power or scheduling them at times of renewable abundance can cut emissions. The BLOOM vs. GPT-3 comparison illustrates this: by training on France’s mostly nuclear (low-carbon) grid, BLOOM’s team kept emissions an order of magnitude lower than an equivalent training on a coal-heavy grid . Similarly, deploying models on data centers fed by renewables can shrink the footprint of each inference. Some companies are even exploring novel solutions like green data center design – for example, Microsoft tested underwater data center pods cooled by seawater and powered by offshore wind, to both reduce cooling energy and tap into renewable power directly . As renewable energy becomes more prevalent on grids, the “fuel” powering AI will gradually get cleaner; however, until a full transition is achieved, the mix of renewable vs. fossil electricity remains a key determinant of AI’s carbon impact.

Industry Efforts to Reduce AI’s Environmental Footprint

Recognizing these challenges, the tech industry and research community have launched multiple initiatives to make AI more sustainable. Key efforts include:

• More Efficient AI Hardware: Companies are designing specialized chips (GPUs, TPUs, and AI accelerators) that deliver more computations per watt of power. For instance, Google’s latest Tensor Processing Unit chips (TPU v4 and the new “Trillium” TPU) achieved a 3× improvement in carbon efficiency for the same AI workloads compared to two generations ago (How Google Tripled AI Chip Carbon Efficiency | Sustainability Magazine). By improving hardware, each training run or inference uses less energy, directly cutting emissions. Google’s study noted that over 70% of an AI chip’s lifetime emissions come from the electricity it consumes in operation, so boosting energy efficiency and lowering power draw has a big climate payoff. Hardware and cloud providers are also optimizing data center layouts and cooling systems to reduce waste – from advanced liquid cooling and heat recycling to raising server room temperatures – all to improve the Power Usage Effectiveness (PUE) and curb excess energy use.

• Optimized Software and Models: Researchers are developing algorithms and model architectures that require less computation. Techniques like model pruning (removing unnecessary parameters), quantization (using lower-precision calculations), and knowledge distillation (training smaller models to mimic large ones) can maintain high AI performance while using fewer operations. Such optimizations can drastically cut the energy needed for training and inference. Google, for example, mentioned “model optimization” as a pillar of managing AI’s environmental impact alongside efficient infrastructure. In practice, using a smaller or more efficient model for a task can save energy; one Google analysis found that using existing simpler models for easy queries and reserving complex AI only for hard queries could save significant power . Many AI frameworks now also include energy-efficient modes and libraries to make it easier for developers to reduce computational load.

• Renewable Energy Adoption: As noted, AI firms are heavily investing in renewable energy. Data center operators increasingly power their servers with wind, solar, hydro, or nuclear energy sources to cut direct emissions. This includes on-site installations (like solar panels on big data centers) and long-term contracts to fund renewable projects that supply the grid. By 2030, the goal is for major cloud providers to be running on carbon-free energy around the clock . In the interim, companies often purchase renewable energy certificates (RECs) or use other mechanisms to offset the grid mix. When models are trained or hosted in a region like Scandinavia (with abundant renewable power) or at times of day when green energy is plentiful, their effective carbon footprint per computation drops. Some AI clusters even perform “carbon-aware” scheduling – timing non-urgent training tasks for periods of high renewable supply or lower grid carbon intensity.

• Carbon Offsets and “Carbon Neutral” Pledges: Several AI-leading companies have also turned to carbon offset programs to compensate for emissions they can’t eliminate. This means funding projects that remove or avoid an equivalent amount of CO₂ elsewhere (such as reforestation or renewable energy projects) to balance out the emissions from AI workloads. Meta (Facebook), for example, has noted that it offset the CO₂ emissions from training its models like LLaMA. These offsets can, in theory, neutralize the climate impact of AI computations on paper. Likewise, many cloud providers that run AI services claim to be carbon-neutral today thanks to purchasing offsets or credits for all their fossil-based emissions. However, it’s worth noting that offsets do not reduce the AI models’ own energy consumption – they are a separate investment to mitigate the effect of those emissions elsewhere.

• Transparency and Monitoring Initiatives: An emerging effort is to measure and disclose AI energy use and emissions more rigorously. “You can’t solve a problem if you can’t measure it,” as one report put it . To this end, groups have developed tools like the AI carbon tracker by Stanford/MosaicML (formerly by Facebook and others) which logs energy use and carbon emissions during model training . Microsoft has an Emissions Impact Dashboard for Azure cloud users to track the carbon footprint of their AI workloads . Google’s recent research even introduced a metric called Compute Carbon Intensity (CCI) – measuring grams of CO₂ per unit of AI computation – to standardize how we gauge hardware and model efficiency across the industry. By increasing transparency, the industry aims to pinpoint inefficiencies and encourage competition on “green” metrics (not just performance metrics). This also ties into broader corporate sustainability efforts, where companies include data center and AI emissions in their environmental reports and set targets to reduce them.

Collectively, these efforts – from better chips and code to green power and offsets – are the tech sector’s response to mitigate AI’s environmental impact. They have yielded some success (for example, newer GPU/TPU hardware can do more with the same energy, and many AI firms report higher renewable energy usage), but the rapid growth in AI demand continues to pose a challenge, as discussed next.

Criticisms and Challenges of “Green AI” Solutions

While the AI industry is taking steps to go greener, critical perspectives point out that these solutions may not be keeping pace with the problem – and in some cases could be seen as greenwashing. One concern is the potential rebound effect: as hardware and algorithms become more efficient, it might simply enable far more widespread use of AI, canceling out efficiency gains with a larger volume of computations. Historically, in fields like transportation and lighting, improved efficiency often led to lower costs and thus higher consumption (known as Jevon’s Paradox). Some experts worry the same could happen with AI: if running GPT-4 becomes 2× more efficient, but we start using it 4× as much in every app, total energy use (and emissions) still go up. Indeed, we’ve seen model sizes and usage escalating so fast that total compute is doubling much faster than efficiency is improving. This outpaces many of the sustainability measures being implemented.

Another critique targets the heavy reliance on carbon offsets. Environmental analysts caution that buying offsets cannot substitute for directly reducing energy consumption and emissions. Offsets can be a useful tool, but if a company simply continues “business-as-usual” – powering AI on fossil electricity – and then buys cheap carbon credits, it may be masking rather than solving the problem (Is Carbon Offset a Form of Greenwashing? | Earth.Org). Climate organizations like Greenpeace and WWF have warned that some offset programs give firms a “license to pollute” and create a false impression of sustainability. In the AI context, if companies claim their models are “100% carbon neutral” via offsets but are not actually switching to cleaner energy or improving efficiency, critics call this greenwashing. They argue that offsets should be a last resort after reducing one’s own footprint, not an excuse to avoid doing so. Similarly, even claims of 100% renewable energy have nuances – often it means the company purchased enough renewable energy credits annually, but it may still draw on some fossil power from the grid at peak times. Without 24/7 carbon-free power, there can be gaps between claims and reality, which skeptics highlight.

There’s also a transparency gap that makes it hard to verify progress. Many AI developers (especially those in the private sector) do not disclose the energy use or carbon emissions of their model training and deployments. For instance, OpenAI has not released detailed emissions data for GPT-4; most figures in the public domain are estimates by third parties. “Currently, there is a lack of quality data on the energy consumed by AI models,” Greenpeace noted in early 2024, emphasizing the need for standardized reporting ( Greenpeace USA Endorses Bill to Assess AI Environmental Impact - Greenpeace - Greenpeace ). Without clear disclosure, it’s difficult for independent experts or regulators to differentiate genuine efficiency improvements from mere PR. This opacity feeds skepticism. Investors and watchdogs are starting to ask pointed questions – if a tech giant claims to be carbon-neutral while massively expanding AI operations, are they truly cutting emissions or just offsetting them on paper? (The Hidden Environmental Costs of Tech Giants’ AI Investments | CFA Institute Enterprising Investor) Some analysts note that companies’ reported carbon footprints have not spiked as much as their AI energy use, implying they might be banking on purchased credits or accounting tactics. This scrutiny adds pressure on companies to prove that their “green AI” initiatives are effective and not just symbolic.

Finally, experts urge a broader conversation about prudence in AI usage. The impressive capabilities of large models can lead to a “use AI for everything” mindset, but researchers like Columbia University’s Clifford Stein argue we should weigh the benefits against the energy costs. “We should be developing tools to think about if it’s worth using these large language models given how much energy they’re consuming,” Stein said, suggesting that for some applications a smaller or more efficient method might suffice . In essence, a critical perspective is that bigger isn’t always better if it comes with outsized carbon emissions – and the AI field may need a cultural shift toward valuing energy efficiency and climate impact alongside raw performance.

Emerging Policies and Regulations for AI Sustainability

Policymakers have begun to take note of AI’s growing carbon footprint, and there are nascent efforts to regulate or guide AI development with sustainability in mind. In the United States, a notable proposal is the Artificial Intelligence Environmental Impacts Act of 2024, introduced by Senators Ed Markey (D-MA) and Martin Heinrich (D-NM). This bill, endorsed by environmental groups like Greenpeace, would direct the government to study AI’s environmental footprint and create a standardized tracking system for AI energy use and emissions. The goal is to increase transparency and provide data that could inform future regulations – effectively laying groundwork so that companies might eventually be required to report the carbon impact of training and deploying large AI models. Greenpeace argues that such transparency is a critical first step to hold companies accountable and ensure AI doesn’t “exacerbate the climate crisis” unnoticed. As of early 2024, this Act was in proposal stages; if passed, it could lead to reporting mandates or efficiency standards for AI similar to those in other industries.

Industry players are also calling for clearer standards. In April 2024, Salesforce, a major software company, made headlines by publicly advocating for laws that would require companies to disclose their AI-related carbon emissions (Salesforce Advocates for AI Emissions Disclosure - PracticalESG). Salesforce called on all organizations using general-purpose AI (like large language models) to publish the energy efficiency and carbon footprint of their AI systems, using common metrics. The idea is that standardized disclosure would enable benchmarking and encourage competition to improve (much as fuel economy standards did for cars). It would also empower customers and investors to make informed choices – for example, favoring AI services that are more energy-efficient or run on green energy. This push from Salesforce aligns with the broader ESG (environmental, social, governance) trend: as AI becomes a core part of tech operations, its environmental impact may fall under the scrutiny of regulators and shareholders concerned with climate accountability. Companies with higher emissions could face carbon taxes, higher operating costs, or reputational risks down the line, so there is momentum to get ahead of the issue through self-regulation or proactive policy.

Internationally, discussions have started on including sustainability in AI governance frameworks, though concrete rules are still in early stages. The European Union’s draft AI Act, for instance, has been criticized for “dangerously neglecting environmental risks” of AI, focusing mostly on ethical and safety aspects (The EU's AI Act: Dangerously Neglecting Environmental Risks). Some experts and NGOs in Europe are urging that the final AI Act incorporate at least basic energy efficiency requirements or disclosure for resource-intensive AI systems (The EU AI Act and environmental protection: the case for a missed ...), to align with the EU’s climate goals. Meanwhile, standards bodies like the ITU (International Telecommunication Union) have begun developing guidelines for energy-efficient AI and data centers (US Environmental Social Governance Legal Considerations AI ...). These could eventually translate into best practices or even requirements for companies to optimize algorithms for lower power usage or to use green cloud infrastructure. We’re also seeing the topic surface in global forums – for example, the World Economic Forum’s coalition on AI governance emphasizes the need to balance AI’s benefits with its resource usage and emissions.

In summary, AI sustainability is now on the radar of regulators and stakeholders. Although binding regulations are still few, the combination of proposed laws, industry self-regulation, and public pressure is gradually pushing AI developers toward more transparency and accountability for their environmental impact. The coming years may bring new reporting rules or efficiency standards specifically aimed at large AI models, ensuring that the next generation of AI is not only more powerful, but also more energy-conscious and climate-friendly.

Conclusion and Expert Outlook

The rapid advancement of AI has brought about incredible capabilities – from GPT-4’s fluent dialogues to powerful image generators – but it has also supercharged energy consumption, raising justifiable concerns about carbon emissions. Studies comparing AI’s footprint to real-world benchmarks have served as a wake-up call: training a single large model can emit hundreds or even thousands of tons of CO₂ , and running these models for millions of users compounds the impact. The industry is responding with innovations in chips, software, and data center operations to curb energy use, alongside moves toward renewable energy and carbon offsets to cut net emissions. These efforts have yielded progress (for example, a given AI task today likely uses less energy than the same task a few years ago, thanks to efficiency gains). Yet, as AI deployment explodes, the absolute environmental footprint of AI remains on an upward trajectory – prompting critics to question whether enough is being done, fast enough.

Expert opinions reflect a mix of optimism and caution. On one hand, leaders like Google’s sustainability team see large opportunities to “continue optimising hardware and software for carbon efficiency” and are starting to share metrics that drive competition on sustainability. On the other hand, scientists and environmental advocates urge moderation and transparency – asking AI developers to be mindful of the trade-offs and to avoid unfettered growth that could undermine climate goals . The coming balance will likely require both technological and policy solutions: smarter engineering to reduce energy per AI operation, and sensible regulations or standards to align AI’s growth with global sustainability targets.

In conclusion, large AI models do have a significant carbon footprint today, comparable to heavy industrial activities in some cases. Running advanced AI is not “virtual” in its impact – it draws electricity that may come from fossil fuels, and thus has a real emissions cost. However, this impact is not fixed in stone. Through concerted efforts – from switching to clean energy and improving efficiency, to possibly rethinking how and when we use giant AI models – the tech industry aims to shrink AI’s carbon footprint even as the technology proliferates. The challenge will be ensuring these green initiatives are effective and scalable, not just token gestures. As policymakers, companies, and researchers converge on this issue, the hope is that future AI breakthroughs will be measured not only by their intelligence, but also by their sustainability, allowing innovation to proceed without accelerating the climate crisis.