PoV #10: Unveiling the Hidden Environmental Costs of Generative AI

Generative AI, with its enchanting capabilities exemplified by models such as ChatGPT, BERT, LaMDA, GPT-3, DALL-E-2, MidJourney, and Stable Diffusion, has undeniably captivated the world. However, amidst the awe and admiration for these technological marvels, a crucial facet often relegated to obscurity is their concealed environmental footprint. The development and deployment of these AI systems exact a substantial toll on our planet's resources, primarily in the realms of energy consumption and the concomitant greenhouse gas emissions. While these AI tools are on the cusp of mainstream acceptance, it is imperative to recognize that these environmental costs loom large on the horizon, poised to burgeon dramatically.

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At the nexus of this issue stands the data center industry, a linchpin for the storage and management of colossal volumes of information and communication technology systems. Paradoxically, this industry that fuels our digital age also contributes significantly to global greenhouse gas emissions, accounting for approximately 2-3% of the total. The exponential growth in global data, doubling in size every two years, imposes Herculean demands on data center servers. These servers, in turn, necessitate prodigious amounts of energy and water for cooling and generating non-renewable electricity to power computing servers, equipment, and cooling systems. Such is the scale of energy consumption in this sector that it claims approximately 7% of Denmark's electricity usage and 2.8% of that in the United States.

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Foremost among the creators and operators of renowned generative AI models are the "hyperscale" cloud providers. These technology behemoths, fueled by thousands of servers, cast a long shadow with their substantial carbon footprint. What exacerbates this environmental impact is the predilection of these models for running on graphics processing unit (GPU) chips, known for their voracious appetite for energy, consuming 10-15 times more than traditional central processing units (CPUs). Presently, Amazon AWS, Google Cloud, and Microsoft Azure reign supreme in this hyperscale cloud provider domain.

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To comprehensively fathom the environmental ramifications of ChatGPT from a carbon footprint perspective, a foray into the lifecycle of carbon emissions entwined with machine learning (ML) models becomes imperative. This comprehension is not just an academic exercise; it forms the bedrock for rendering generative AI more ecologically sound through judicious reductions in energy consumption.

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Generative AI, having recently stepped into the public's limelight, heralds an era of exciting innovations and profound discussions across various industries. Nevertheless, one disconcerting refrain that remains conspicuously muted is the discourse surrounding the environmental consequences of generative AI. It is high time that we shed light on this matter, especially considering that the information technology industry, already carrying the onus of two to four percent of total global greenhouse gas emissions, threatens to eclipse even the aviation sector in terms of its carbon footprint. Furthermore, projections suggest that by 2040, the energy demand for computing could surpass our current energy production capacity.

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The energy consumed in the world of AI is chiefly categorized into two domains: training and inference. Training, the initial phase, entails AI learning to discern patterns and relationships within vast datasets. Naturally, models with a greater number of parameters yield more accurate results during the subsequent inference phase. However, training on expansive datasets or with numerous parameters demands an exponentially greater computational power.

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On the flip side, the inference phase involves the utilization of the AI model post-training to make predictions or generate content in response to prompts. Inferences, while individually less energy-intensive than training, accumulate over time, particularly when models serve in the cloud and cater to millions of users concurrently. The cumulative consumption of computing resources in such scenarios can be formidable.

?The Energy Costs of Generative AI Training

When we delve deeper into the energy consumption entailed by the training of generative AI models, the enormity of the challenge becomes apparent. Training on such models represents a staggering drain on computational power and energy resources, surpassing the requirements of predictive AI technologies. To put this into perspective, the precursor to ChatGPT, GPT-3, was estimated by researchers at Google and UC Berkeley to have emitted approximately 552 tCO2e (carbon dioxide equivalent emissions) or consumed 1,287 MWh (megawatt-hours) of energy during its training. To contextualize this figure, it equates to the energy consumption of 121 average U.S. households over an entire year.

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Similarly, Meta's OPT-175B model was developed with an estimated carbon footprint of 75 tCO2e, which effectively doubles to approximately 150 tCO2e when various factors such as ablations, baselines, and downtime are considered. Meta's researchers have illuminated a significant surge in data ingestion bandwidth demand and training infrastructure capacity over a relatively short period, underscoring the exponential growth in AI training requirements.

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A study by semiconductor analysis firm SemiAnalysis, as elucidated in a commentary published in the journal Joule on October 10 and authored by de Vries, provides estimations of the energy consumption associated with the use of generative AI in large-scale applications, akin to ChatGPT. This research posits that employing generative AI for each Google search would necessitate an astounding number of Nvidia's A100 HGX servers—more than 500,000 of them, constituting a staggering 4.1 million graphics processing units (GPUs). This scale of adoption would translate into a daily electricity consumption of 80 GWh and an annual consumption of 29.2 TWh, under the assumption of a power demand of 6.5 kW per server.

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However, it is imperative to acknowledge the practicality of such widespread adoption using current hardware and software. Economic constraints and supply chain limitations render this scenario unlikely. The colossal volume of Nvidia servers required for such a scale of AI utilization simply does not exist at present, and the cost of manufacturing such a multitude could potentially soar to an astronomical $100 billion.

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The commentary also astutely underscores that while the swift proliferation of AI technology theoretically could precipitate a significant surge in energy consumption for companies like Google, various resource constraints are poised to serve as bulwarks against these worst-case scenarios becoming a reality.

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When examining the trends in energy consumption in U.S. data centers, it becomes evident that the demand for power from operational and planned data centers in U.S. power markets is projected to reach approximately 30,694 MW once all the planned data centers reach full operational capacity. This projection is rooted in a meticulous analysis of data from 451 Research, a constituent of S&P Global Market Intelligence. Within this capacity, investor-owned utilities are expected to supply a substantial 20,619 MW.

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To provide some context, it is worthwhile to note that the total power demand in the U.S. Lower 48 is forecasted to be around 473 GW in 2023, with a projected rise to approximately 482 GW in 2027, according to an S&P Global Commodity Insights analytics forecast. However, it is important to highlight that these forecasts do not presently account for significant adjustments attributed to the adoption of AI.

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Significant perturbations in power demand trends, if they do materialize, are likely to manifest through the actions of utilities serving large data centers. Dominion Energy, for instance, caters to the world's largest data center market located in Loudoun County, Virginia. Over the years, the demand for electricity from data centers in Virginia has experienced a meteoric rise, surging by about 500% from 2013 to 2022.

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It is noteworthy that the power demand inherent in training AI models exhibits an initial high concentration and is more pronounced than traditional data center applications. AI training can exact a power consumption that is two to three times greater than that of conventional data center applications. Addressing this demand necessitates the development of new technologies in power converters to effectively accommodate these surges.

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In summary, the specter of energy consumption looming over generative AI and large-scale data centers is a matter that warrants our collective concern. While the theoretical energy demands of AI are nothing short of colossal, various factors, including economic constraints and the constrictions of supply chains, are poised to exert a mitigating influence on the widespread adoption of these technologies. Nevertheless, the burgeoning growth of AI and data centers remains poised to continue its inexorable march, thereby necessitating innovative solutions to harmonize technological progress with the imperatives of environmental sustainability.

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A disconcerting trend emerges from a 2018 analysis conducted by OpenAI, the creators of ChatGPT. This analysis reveals an exponential rise in the consumption of computational resources during the largest AI training runs. The doubling time for this resource consumption stands at a mere 3.4 months, a stark departure from Moore's Law, which predicted a doubling of compute efficiency every two years.

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In the realm of inference, there exists a dearth of data regarding the energy consumption and environmental impact of generative AI. Nevertheless, recent research emanating from Northeastern and MIT paints an ominous picture, suggesting that inference wields a considerably greater influence on energy consumption when compared to training. Estimates from AWS and Nvidia further illuminate this issue, contending that inference can account for up to 80-90% of the total operational costs in the realm of deep learning.

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While precise figures may still remain elusive, it is manifestly evident that the proliferation of generative AI is inexorably steering us towards a future marked by a surge in carbon emissions within the information technology sector. This environmental challenge extends beyond mere carbon emissions and spills over into the realm of water resources. For instance, the training of GPT-3 was estimated to require a staggering 700,000 liters of clean freshwater. To put this in perspective, ChatGPT's inference alone could deplete a 500 mL bottle of water during a relatively brief conversation comprising 20-50 questions and answers.

?Deploying AI Intentionally

In light of these formidable environmental challenges, companies stand at a crossroads where they must commit to deploying AI thoughtfully and with a keen eye on optimizing its efficiency. The carbon footprint of generative AI models can be dissected into three distinct components:

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1. Training the Model: This phase, characterized by training large generative models, stands as the most energy-intensive. Models brimming with parameters and vast training data tend to exact a greater toll in terms of energy consumption and carbon emissions. For instance, the precursor to ChatGPT, GPT-3, boasts a staggering 175 billion model parameters and was trained on an expansive corpus of over 500 billion words of text. Recent generative AI models have witnessed a ten to a hundred-fold increase in the computational power required for training when compared to their predecessors.

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2. Running Inference: Inference sessions, while individually less energy-intensive, accumulate over time, especially when models are deployed in the cloud and cater to millions of users concurrently. Ongoing inference processing after training constitutes a significant portion of the energy cost.

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3. Manufacturing Hardware: The energy costs associated with the production of hardware, including the intricate GPU chips and servers that underpin AI model execution, are substantial. Studies have illuminated the fact that manufacturing accounts for a significant portion of a computer's overall energy usage.

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Efforts to Render AI Greener:

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In the face of these formidable environmental challenges, a concerted movement is afoot to infuse sustainability into AI modeling, deployment, and usage. The strategies set forth below pave the way towards this laudable goal:

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1. Utilize Existing Models: Companies should eschew the creation of their own large models from scratch, for this process is voracious in its consumption of energy. Instead, they can harness existing large language and image models proffered by established providers.

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2. Fine-Tune Existing Models: In scenarios where customization is a necessity, organizations should opt for fine-tuning existing models rather than embarking on the arduous journey of training entirely new ones. Fine-tuning not only consumes significantly less energy but also allows tailoring to specific content domains.

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3. Energy-Efficient Methods: The adoption of energy-conserving computational approaches, exemplified by TinyML, holds the promise of enabling ML models to run on low-powered edge devices without the need for extensive data center processing.

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4. Value-Based Usage: The judicious employment of large AI models should be contingent on the value they bring to the table. A careful evaluation of the trade-off between increased accuracy and higher energy consumption is paramount. Sometimes, simpler solutions may suffice.

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5. Discerning AI Usage: AI should be wielded judiciously, with a preference for applications where it confers substantial benefits, such as medical diagnostics and natural disaster prediction, as opposed to frivolous purposes.

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6. Eco-Friendly Data Centers: The selection of cloud providers and data centers that place a premium on environmentally friendly power resources goes a long way in diminishing the carbon footprint associated with AI operations.

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7. Resource Reuse: The promotion of the reuse of models and resources offers a practical means of diminishing the incessant need for continual training and the manufacture of new hardware.

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8. Carbon Monitoring: The widespread adoption of carbon monitoring practices across research laboratories, AI vendors, and firms leveraging AI is indispensable. This practice not only serves to assess carbon footprints but also facilitates transparency in responsible AI development.

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In conclusion, while generative AI models unfurl a tapestry of immense promise, they simultaneously cast a shadow of concern with their environmental impact. The adoption of sustainable practices, coupled with a concerted drive to reduce energy consumption, emerges as the clarion call to ensure that AI technologies contribute to a greener and more sustainable future for our planet. To ignore these concerns would be tantamount to imperiling our capacity for meaningful debates regarding the long-term implications of AI on the habitability of our Earth.

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