ChatGPT vs. DeepSeek: which AI model Is more sustainable?
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Published on: 2025-02-03 13:46:54
Source: Latest from TechRadar US in News,opinion
Introduction
By now, even casual observers of the tech world are well aware of ChatGPT, OpenAI’s dazzling contribution to artificial intelligence. Its ability to generate coherent, on-point responses has upended online research and sparked endless speculation about AI’s growing role in our everyday lives.
A recent rising challenger, China’s opensource AI-powered chatbot, DeepSeek, has drawn its own intrigue, promising to run more efficiently and be better suited to non-English users than its American competitor.
Yet in the rush to assess its functionality, adoption, and potential geopolitical sway, one pressing question seems to have been sidelined: how do the environmental credentials of these ChatGPT and DeepSeek compare?
Digital Sustainability Specialist at Jisc.
Where It All Began: A Look at ChatGPT and DeepSeek’s Origins
ChatGPT
ChatGPT’s meteoric rise began in late 2022, with OpenAI and Microsoft forming a high-profile alliance to scale it via Azure’s cloud services. Every iteration of the GPT architecture, however, comes at a steep environmental price. Training such a colossal model requires immense computing power, and the subsequent energy use has raised uncomfortable questions about its carbon footprint.
DeepSeek
While DeepSeek hasn’t yet become a household name to the extent ChatGPT has, it’s earning a reputation as a leaner, more multilingual competitor. It uses techniques like pruning (removing unnecessary parts of the model to reduce size and improve efficiency), model distillation (training a smaller “student” model to imitate a larger “teacher” model), and algorithmic streamlining (optimizing each step of the computation process to minimize wasted resources and improve overall performance) – all intended to cut down on resources and associated costs.
The theory goes that an AI needing fewer GPUs should, in principle, consume less energy overall. Yet details on its total environmental impact remain conspicuously thin, leaving observers to wonder if DeepSeek’s operational gains could truly deliver on the sustainability front.
Energy and Carbon Emissions
The most glaring environmental toll for both models lies in the power needed to train them. Early estimates suggest that rolling out ChatGPT’s latest language model, GPT4, demanded colossal GPU capacity for weeks on end.
DeepSeek, meanwhile, claims to require fewer high-end chips, potentially reducing its total electricity draw.
Data Centers and Energy Sources
Powering ChatGPT on Microsoft’s Azure platform has its upsides and downsides. Microsoft is working to become carbon-negative by 2030, underpinned by investments in green energy and carbon capture. Yet many of its data centers remain tethered to non-renewable energy grids, and the manufacture of sophisticated AI chips is itself resource-intensive.
DeepSeek appears to rely on Alibaba Cloud, China’s most prominent cloud provider, which has set similar targets for carbon neutrality. But China’s national grid continues to rely heavily on coal, meaning the actual environmental impact might be more significant unless DeepSeek is sited in locations rich in renewable infrastructure. That said, DeepSeek’s focus on efficiency might still make it less carbon-intensive overall.
Water Usage and Cooling
Running giant clusters of GPUs produces heat – lots of it. Data centres typically use vast amounts of water for cooling, especially in regions with high temperatures. Microsoft has come under fire for consuming billions of liters of water, some of which goes towards cooling the hardware behind AI operations.
Information on DeepSeek’s water footprint is scant. If Alibaba Cloud’s newer facilities use advanced cooling methods – such as immersion cooling (submerging servers in a thermally conductive liquid to dissipate heat more efficiently) – DeepSeek might fare better in terms of water usage. But with so little public data on its processes, it’s difficult to measure how it stacks up against ChatGPT on this front.
The Hidden Cost of E-Waste
The relentless pace of AI hardware development means GPUs and other accelerators can quickly become obsolete. ChatGPT’s operations, involving cutting-edge equipment, likely generate a rising tide of e-waste, though precise figures are elusive.
In principle, DeepSeek’s more frugal approach implies fewer chips, which could mean slower turnover and less waste. Still, this remains an educated guess until there’s more visibility into how DeepSeek’s hardware ecosystem is managed.
Where Do They Stand?
At first glance, OpenAI’s partnership with Microsoft suggests ChatGPT might stand to benefit from a more environmentally conscious framework – provided that Microsoft’s grand sustainability promises translate into meaningful progress on the ground. DeepSeek, meanwhile, must grapple with a coal-reliant grid in China, yet its drive for efficiency could place it in a better position to curb overall energy consumption per operation.
That said, the U.S. is hardly a clean-energy haven either. While Microsoft has pledged to go carbon-negative by 2030, America remains one of the world’s largest consumers of fossil fuels, with coal still powering parts of its grid. Moreover, political shifts could slow progress: the resurgence of a “drill, baby, drill” mentality in Republican energy rhetoric suggests a renewed push for oil and gas, potentially undermining AI’s green ambitions.
Ultimately, AI is hurtling forward at breakneck speed, but the environmental ramifications lag far behind in public scrutiny. As these systems weave themselves ever deeper into our politics, economy, and daily interactions, the debate on their energy sources, water usage, and hardware footprints must become more transparent. If the world’s appetite for AI is unstoppable, then so too must be our commitment to holding its creators accountable for the planet’s long-term well-being. That responsibility extends not just to China and the U.S. and every nation where AI is trained, deployed, and powered.
We’ve created a comprehensive list of the best AI tools.
This article was produced as part of TechRadarPro’s Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here:
Top Features
By now, even casual observers of the tech world are well aware of ChatGPT, OpenAI’s dazzling contribution to artificial intelligence. Its ability to generate coherent, on-point responses has upended online research and sparked endless speculation about AI’s growing role in our everyday lives.
A recent rising challenger, China’s opensource AI-powered chatbot, DeepSeek, has drawn its own intrigue, promising to run more efficiently and be better suited to non-English users than its American competitor.
Yet in the rush to assess its functionality, adoption, and potential geopolitical sway, one pressing question seems to have been sidelined: how do the environmental credentials of these ChatGPT and DeepSeek compare?
Digital Sustainability Specialist at Jisc.
Where It All Began: A Look at ChatGPT and DeepSeek’s Origins
ChatGPT
ChatGPT’s meteoric rise began in late 2022, with OpenAI and Microsoft forming a high-profile alliance to scale it via Azure’s cloud services. Every iteration of the GPT architecture, however, comes at a steep environmental price. Training such a colossal model requires immense computing power, and the subsequent energy use has raised uncomfortable questions about its carbon footprint.
DeepSeek
While DeepSeek hasn’t yet become a household name to the extent ChatGPT has, it’s earning a reputation as a leaner, more multilingual competitor. It uses techniques like pruning (removing unnecessary parts of the model to reduce size and improve efficiency), model distillation (training a smaller “student” model to imitate a larger “teacher” model), and algorithmic streamlining (optimizing each step of the computation process to minimize wasted resources and improve overall performance) – all intended to cut down on resources and associated costs.
The theory goes that an AI needing fewer GPUs should, in principle, consume less energy overall. Yet details on its total environmental impact remain conspicuously thin, leaving observers to wonder if DeepSeek’s operational gains could truly deliver on the sustainability front.
Energy and Carbon Emissions
The most glaring environmental toll for both models lies in the power needed to train them. Early estimates suggest that rolling out ChatGPT’s latest language model, GPT4, demanded colossal GPU capacity for weeks on end.
DeepSeek, meanwhile, claims to require fewer high-end chips, potentially reducing its total electricity draw.
Data Centers and Energy Sources
Powering ChatGPT on Microsoft’s Azure platform has its upsides and downsides. Microsoft is working to become carbon-negative by 2030, underpinned by investments in green energy and carbon capture. Yet many of its data centers remain tethered to non-renewable energy grids, and the manufacture of sophisticated AI chips is itself resource-intensive.
DeepSeek appears to rely on Alibaba Cloud, China’s most prominent cloud provider, which has set similar targets for carbon neutrality. But China’s national grid continues to rely heavily on coal, meaning the actual environmental impact might be more significant unless DeepSeek is sited in locations rich in renewable infrastructure. That said, DeepSeek’s focus on efficiency might still make it less carbon-intensive overall.
Water Usage and Cooling
Running giant clusters of GPUs produces heat – lots of it. Data centres typically use vast amounts of water for cooling, especially in regions with high temperatures. Microsoft has come under fire for consuming billions of liters of water, some of which goes towards cooling the hardware behind AI operations.
Information on DeepSeek’s water footprint is scant. If Alibaba Cloud’s newer facilities use advanced cooling methods – such as immersion cooling (submerging servers in a thermally conductive liquid to dissipate heat more efficiently) – DeepSeek might fare better in terms of water usage. But with so little public data on its processes, it’s difficult to measure how it stacks up against ChatGPT on this front.
The Hidden Cost of E-Waste
The relentless pace of AI hardware development means GPUs and other accelerators can quickly become obsolete. ChatGPT’s operations, involving cutting-edge equipment, likely generate a rising tide of e-waste, though precise figures are elusive.
In principle, DeepSeek’s more frugal approach implies fewer chips, which could mean slower turnover and less waste. Still, this remains an educated guess until there’s more visibility into how DeepSeek’s hardware ecosystem is managed.
Where Do They Stand?
At first glance, OpenAI’s partnership with Microsoft suggests ChatGPT might stand to benefit from a more environmentally conscious framework – provided that Microsoft’s grand sustainability promises translate into meaningful progress on the ground. DeepSeek, meanwhile, must grapple with a coal-reliant grid in China, yet its drive for efficiency could place it in a better position to curb overall energy consumption per operation.
That said, the U.S. is hardly a clean-energy haven either. While Microsoft has pledged to go carbon-negative by 2030, America remains one of the world’s largest consumers of fossil fuels, with coal still powering parts of its grid. Moreover, political shifts could slow progress: the resurgence of a “drill, baby, drill” mentality in Republican energy rhetoric suggests a renewed push for oil and gas, potentially undermining AI’s green ambitions.
Ultimately, AI is hurtling forward at breakneck speed, but the environmental ramifications lag far behind in public scrutiny. As these systems weave themselves ever deeper into our politics, economy, and daily interactions, the debate on their energy sources, water usage, and hardware footprints must become more transparent. If the world’s appetite for AI is unstoppable, then so too must be our commitment to holding its creators accountable for the planet’s long-term well-being. That responsibility extends not just to China and the U.S. and every nation where AI is trained, deployed, and powered.
We’ve created a comprehensive list of the best AI tools.
This article was produced as part of TechRadarPro’s Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here:
Pros and Cons
By now, even casual observers of the tech world are well aware of ChatGPT, OpenAI’s dazzling contribution to artificial intelligence. Its ability to generate coherent, on-point responses has upended online research and sparked endless speculation about AI’s growing role in our everyday lives.
A recent rising challenger, China’s opensource AI-powered chatbot, DeepSeek, has drawn its own intrigue, promising to run more efficiently and be better suited to non-English users than its American competitor.
Yet in the rush to assess its functionality, adoption, and potential geopolitical sway, one pressing question seems to have been sidelined: how do the environmental credentials of these ChatGPT and DeepSeek compare?
Digital Sustainability Specialist at Jisc.
Where It All Began: A Look at ChatGPT and DeepSeek’s Origins
ChatGPT
ChatGPT’s meteoric rise began in late 2022, with OpenAI and Microsoft forming a high-profile alliance to scale it via Azure’s cloud services. Every iteration of the GPT architecture, however, comes at a steep environmental price. Training such a colossal model requires immense computing power, and the subsequent energy use has raised uncomfortable questions about its carbon footprint.
DeepSeek
While DeepSeek hasn’t yet become a household name to the extent ChatGPT has, it’s earning a reputation as a leaner, more multilingual competitor. It uses techniques like pruning (removing unnecessary parts of the model to reduce size and improve efficiency), model distillation (training a smaller “student” model to imitate a larger “teacher” model), and algorithmic streamlining (optimizing each step of the computation process to minimize wasted resources and improve overall performance) – all intended to cut down on resources and associated costs.
The theory goes that an AI needing fewer GPUs should, in principle, consume less energy overall. Yet details on its total environmental impact remain conspicuously thin, leaving observers to wonder if DeepSeek’s operational gains could truly deliver on the sustainability front.
Energy and Carbon Emissions
The most glaring environmental toll for both models lies in the power needed to train them. Early estimates suggest that rolling out ChatGPT’s latest language model, GPT4, demanded colossal GPU capacity for weeks on end.
DeepSeek, meanwhile, claims to require fewer high-end chips, potentially reducing its total electricity draw.
Data Centers and Energy Sources
Powering ChatGPT on Microsoft’s Azure platform has its upsides and downsides. Microsoft is working to become carbon-negative by 2030, underpinned by investments in green energy and carbon capture. Yet many of its data centers remain tethered to non-renewable energy grids, and the manufacture of sophisticated AI chips is itself resource-intensive.
DeepSeek appears to rely on Alibaba Cloud, China’s most prominent cloud provider, which has set similar targets for carbon neutrality. But China’s national grid continues to rely heavily on coal, meaning the actual environmental impact might be more significant unless DeepSeek is sited in locations rich in renewable infrastructure. That said, DeepSeek’s focus on efficiency might still make it less carbon-intensive overall.
Water Usage and Cooling
Running giant clusters of GPUs produces heat – lots of it. Data centres typically use vast amounts of water for cooling, especially in regions with high temperatures. Microsoft has come under fire for consuming billions of liters of water, some of which goes towards cooling the hardware behind AI operations.
Information on DeepSeek’s water footprint is scant. If Alibaba Cloud’s newer facilities use advanced cooling methods – such as immersion cooling (submerging servers in a thermally conductive liquid to dissipate heat more efficiently) – DeepSeek might fare better in terms of water usage. But with so little public data on its processes, it’s difficult to measure how it stacks up against ChatGPT on this front.
The Hidden Cost of E-Waste
The relentless pace of AI hardware development means GPUs and other accelerators can quickly become obsolete. ChatGPT’s operations, involving cutting-edge equipment, likely generate a rising tide of e-waste, though precise figures are elusive.
In principle, DeepSeek’s more frugal approach implies fewer chips, which could mean slower turnover and less waste. Still, this remains an educated guess until there’s more visibility into how DeepSeek’s hardware ecosystem is managed.
Where Do They Stand?
At first glance, OpenAI’s partnership with Microsoft suggests ChatGPT might stand to benefit from a more environmentally conscious framework – provided that Microsoft’s grand sustainability promises translate into meaningful progress on the ground. DeepSeek, meanwhile, must grapple with a coal-reliant grid in China, yet its drive for efficiency could place it in a better position to curb overall energy consumption per operation.
That said, the U.S. is hardly a clean-energy haven either. While Microsoft has pledged to go carbon-negative by 2030, America remains one of the world’s largest consumers of fossil fuels, with coal still powering parts of its grid. Moreover, political shifts could slow progress: the resurgence of a “drill, baby, drill” mentality in Republican energy rhetoric suggests a renewed push for oil and gas, potentially undermining AI’s green ambitions.
Ultimately, AI is hurtling forward at breakneck speed, but the environmental ramifications lag far behind in public scrutiny. As these systems weave themselves ever deeper into our politics, economy, and daily interactions, the debate on their energy sources, water usage, and hardware footprints must become more transparent. If the world’s appetite for AI is unstoppable, then so too must be our commitment to holding its creators accountable for the planet’s long-term well-being. That responsibility extends not just to China and the U.S. and every nation where AI is trained, deployed, and powered.
We’ve created a comprehensive list of the best AI tools.
This article was produced as part of TechRadarPro’s Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here:
User Reviews
By now, even casual observers of the tech world are well aware of ChatGPT, OpenAI’s dazzling contribution to artificial intelligence. Its ability to generate coherent, on-point responses has upended online research and sparked endless speculation about AI’s growing role in our everyday lives.
A recent rising challenger, China’s opensource AI-powered chatbot, DeepSeek, has drawn its own intrigue, promising to run more efficiently and be better suited to non-English users than its American competitor.
Yet in the rush to assess its functionality, adoption, and potential geopolitical sway, one pressing question seems to have been sidelined: how do the environmental credentials of these ChatGPT and DeepSeek compare?
Digital Sustainability Specialist at Jisc.
Where It All Began: A Look at ChatGPT and DeepSeek’s Origins
ChatGPT
ChatGPT’s meteoric rise began in late 2022, with OpenAI and Microsoft forming a high-profile alliance to scale it via Azure’s cloud services. Every iteration of the GPT architecture, however, comes at a steep environmental price. Training such a colossal model requires immense computing power, and the subsequent energy use has raised uncomfortable questions about its carbon footprint.
DeepSeek
While DeepSeek hasn’t yet become a household name to the extent ChatGPT has, it’s earning a reputation as a leaner, more multilingual competitor. It uses techniques like pruning (removing unnecessary parts of the model to reduce size and improve efficiency), model distillation (training a smaller “student” model to imitate a larger “teacher” model), and algorithmic streamlining (optimizing each step of the computation process to minimize wasted resources and improve overall performance) – all intended to cut down on resources and associated costs.
The theory goes that an AI needing fewer GPUs should, in principle, consume less energy overall. Yet details on its total environmental impact remain conspicuously thin, leaving observers to wonder if DeepSeek’s operational gains could truly deliver on the sustainability front.
Energy and Carbon Emissions
The most glaring environmental toll for both models lies in the power needed to train them. Early estimates suggest that rolling out ChatGPT’s latest language model, GPT4, demanded colossal GPU capacity for weeks on end.
DeepSeek, meanwhile, claims to require fewer high-end chips, potentially reducing its total electricity draw.
Data Centers and Energy Sources
Powering ChatGPT on Microsoft’s Azure platform has its upsides and downsides. Microsoft is working to become carbon-negative by 2030, underpinned by investments in green energy and carbon capture. Yet many of its data centers remain tethered to non-renewable energy grids, and the manufacture of sophisticated AI chips is itself resource-intensive.
DeepSeek appears to rely on Alibaba Cloud, China’s most prominent cloud provider, which has set similar targets for carbon neutrality. But China’s national grid continues to rely heavily on coal, meaning the actual environmental impact might be more significant unless DeepSeek is sited in locations rich in renewable infrastructure. That said, DeepSeek’s focus on efficiency might still make it less carbon-intensive overall.
Water Usage and Cooling
Running giant clusters of GPUs produces heat – lots of it. Data centres typically use vast amounts of water for cooling, especially in regions with high temperatures. Microsoft has come under fire for consuming billions of liters of water, some of which goes towards cooling the hardware behind AI operations.
Information on DeepSeek’s water footprint is scant. If Alibaba Cloud’s newer facilities use advanced cooling methods – such as immersion cooling (submerging servers in a thermally conductive liquid to dissipate heat more efficiently) – DeepSeek might fare better in terms of water usage. But with so little public data on its processes, it’s difficult to measure how it stacks up against ChatGPT on this front.
The Hidden Cost of E-Waste
The relentless pace of AI hardware development means GPUs and other accelerators can quickly become obsolete. ChatGPT’s operations, involving cutting-edge equipment, likely generate a rising tide of e-waste, though precise figures are elusive.
In principle, DeepSeek’s more frugal approach implies fewer chips, which could mean slower turnover and less waste. Still, this remains an educated guess until there’s more visibility into how DeepSeek’s hardware ecosystem is managed.
Where Do They Stand?
At first glance, OpenAI’s partnership with Microsoft suggests ChatGPT might stand to benefit from a more environmentally conscious framework – provided that Microsoft’s grand sustainability promises translate into meaningful progress on the ground. DeepSeek, meanwhile, must grapple with a coal-reliant grid in China, yet its drive for efficiency could place it in a better position to curb overall energy consumption per operation.
That said, the U.S. is hardly a clean-energy haven either. While Microsoft has pledged to go carbon-negative by 2030, America remains one of the world’s largest consumers of fossil fuels, with coal still powering parts of its grid. Moreover, political shifts could slow progress: the resurgence of a “drill, baby, drill” mentality in Republican energy rhetoric suggests a renewed push for oil and gas, potentially undermining AI’s green ambitions.
Ultimately, AI is hurtling forward at breakneck speed, but the environmental ramifications lag far behind in public scrutiny. As these systems weave themselves ever deeper into our politics, economy, and daily interactions, the debate on their energy sources, water usage, and hardware footprints must become more transparent. If the world’s appetite for AI is unstoppable, then so too must be our commitment to holding its creators accountable for the planet’s long-term well-being. That responsibility extends not just to China and the U.S. and every nation where AI is trained, deployed, and powered.
We’ve created a comprehensive list of the best AI tools.
This article was produced as part of TechRadarPro’s Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here:
