How Compute with Hivenet turns sustainability from a buzzword into a design principle.
For more on how transparent pricing supports sustainability, read The economics of the neocloud, which explains how fairness and cost efficiency align with environmental responsibility.
Advanced technologies such as AI, IoT, and innovative chip architectures are driving significant improvements in efficiency and sustainability in the neocloud era.
Why sustainability matters in AI compute
AI is powerful but resource-hungry. Each model trained, each image generated, and each inference request consumes energy. AI model training often requires thousands of graphics processing units (GPUs) running continuously for months, leading to high electricity use. The more models we run, the more energy the world needs to feed them. Traditional clouds scale that demand with new data centers — each one requiring fresh construction, cooling, and energy contracts. Additionally, AI models often rely on fossil fuels as a primary energy source, which significantly contributes to greenhouse gas emissions. Training large language models, in particular, exemplifies this challenge, as it demands extensive GPU usage over prolonged periods. Data centers supporting AI usage are projected to account for up to 20% of global electricity consumption by 2030-2035, further highlighting the urgency of addressing these energy demands. In 2023, data centers consumed 4.4% of U.S. electricity, a figure that underscores the growing energy impact of these facilities. This number could triple by 2028, further exacerbating the environmental challenges posed by AI and data center operations. Data centers generally use about 100-200 times more energy than typical office buildings, further emphasizing their outsized role in global energy consumption. This rapid increase in power demand, infrastructure costs, and environmental impact raises serious concerns for the future of sustainable AI. These energy and environmental impacts represent significant environmental challenges that require innovative solutions to ensure AI development aligns with global climate goals.
That model doesn’t scale ethically or environmentally. It’s why the neocloud approach emerged: distributed, efficient, and fair. The AI-first cloud model behind Compute with Hivenet makes sustainability an operational choice, not an afterthought.
The neocloud model: reuse data centers before build
Compute with Hivenet (Terms of Service) rethinks infrastructure from the ground up. Instead of building more centralized data centers, it connects existing devices and nodes—including a wide variety of computing systems such as data centers, chips, and embedded devices—into a distributed network. Idle GPUs become active again. Energy that would have gone to waste powers real workloads by optimizing the use of available energy resources. The environmental impacts of AI extend beyond energy consumption to include greenhouse gas emissions and e-waste concerns, which Hivenet’s approach helps to mitigate. The short lifespan of GPUs and other high-performance computing components exacerbates the problem of electronic waste, but Hivenet’s reuse-focused model addresses this issue effectively. Cooling systems in AI data centers also require excessive water, impacting regions with water scarcity, but Hivenet’s distributed model reduces reliance on such resource-intensive systems. Each kilowatt hour consumed by a data center requires approximately two liters of water for cooling, further emphasizing the importance of Hivenet’s innovative approach to sustainability. Additionally, the manufacturing of computing hardware like GPUs contributes to environmental degradation through the extraction of rare earth minerals, a challenge that Hivenet’s reuse model helps to alleviate. Transitioning AI data centers to renewable energy sources can help reduce carbon emissions from fossil fuels, further aligning with global sustainability goals. The neocloud model can also integrate specialized hardware like tensor processing units (TPUs) to further improve efficiency and sustainability for large-scale AI workloads.
This principle — reuse before build — drives down emissions and hardware waste. It turns every connected device into part of a sustainable GPU cloud. By reducing the need for new hardware, Hivenet also minimizes the environmental degradation caused by the extraction of rare earth minerals used in manufacturing computing hardware. This approach not only reduces waste but also lessens the ecological footprint of hardware production. The pay-as-you-go model for cloud GPUs further supports this by eliminating the need for heavy upfront capital expenditures on hardware and maintenance costs, making sustainable computing more accessible. However, smaller organizations often face challenges in training AI models due to limited GPU and TPU resources, which can lead to longer training times and higher cumulative energy consumption. Hivenet’s distributed model helps address this by democratizing access to computational power, enabling more efficient and equitable AI development.
Each task completed on Hivenet saves energy that traditional clouds would have spent on cooling or idle capacity. Sustainability isn’t an afterthought; it’s built into the model.
Measuring impact: energy efficiency per watt
Sustainability isn’t just about good intentions — it’s about measurable outcomes. In traditional hyperscale data centers, efficiency is measured by Power Usage Effectiveness (PUE). The lower, the better. But even the best hyperscalers rarely achieve below 1.1. Monitoring and optimizing power consumption in distributed compute environments is crucial for improving overall efficiency and reducing unnecessary energy use. Accurate carbon emissions reporting can shape investments and policies for a more sustainable AI landscape, ensuring that measurable outcomes drive meaningful change. Several organizations are advocating for carbon emissions reporting that is standardized, accurate, and auditable, which could further enhance transparency and accountability in the tech industry. The International Sustainability Standards Board (ISSB) is one such organization working towards standardizing carbon emissions reporting across tech companies, helping to create a unified framework for sustainability metrics.
Hivenet’s distributed approach changes that. By decentralizing compute and leveraging existing resources, it achieves natural efficiency gains. Less cooling. Less idle time. Less waste. The result is a more energy-efficient AI compute infrastructure that scales without environmental trade-offs. Implementing strategies to distribute AI computations across different time zones can further optimize energy use by aligning workloads with periods of peak renewable energy availability, and by applying optimization techniques to improve computational efficiency. Businesses can also dynamically scale GPU resources based on workload demands, minimizing energy consumption and preventing energy waste from over-provisioning. Together, these strategies play a significant role in carbon footprint reduction for AI and cloud computing operations. To learn more, consider these questions to ask before choosing a distributed compute provider.
Green by design, not carbon footprint offset
Many hyperscalers offset emissions through renewable energy certificates or carbon programs. Those help, but they don’t change the fact that new data centers keep being built. The neocloud model avoids the problem at its root. This is especially critical as data centers are projected to consume 20% of global electricity by 2030–2035, straining power grids and increasing environmental pressures. Addressing this growing demand requires innovative solutions like Hivenet’s distributed approach. Hivenet integrates sustainable practices and energy efficient practices throughout its operations, optimizing hardware and infrastructure to minimize environmental impact.
Compute with Hivenet doesn’t just buy offsets — it avoids emissions altogether. Its eco-friendly AI compute network reuses hardware that already exists and runs on energy already available. That’s sustainability by design, not compensation. This reuse of hardware and distributed compute is a key strategy for achieving sustainability in AI compute.
Why distributed means fair
The neocloud’s sustainability isn’t limited to energy. It also touches fairness and digital sovereignty. Distributed systems enable compute access across regions without forcing data into centralized infrastructures. That’s greener, but also fairer — more control, less dependency.
By keeping workloads local, Compute with Hivenet reduces data travel and improves compliance with European privacy standards. It’s a green and sovereign approach that supports both performance and policy. Distributed compute models also facilitate economic co operation between regions and industries, supporting strategic plans for sustainable digital infrastructure on a global scale.
For more on sovereignty, read The future of cloud sovereignty — why the neocloud matters for Europe.
The Role of Research in Sustainability
Research drives sustainability progress in tech, especially as AI and cloud computing reshape our digital world. Computing power demands keep growing, and we need to tackle the environmental impact of data centers, AI models, and their supporting infrastructure. Researchers develop strategies to cut greenhouse gas emissions, improve energy use, and reduce AI development's carbon footprint.
Training AI models consumes massive amounts of energy. Deep learning and generative AI need huge computational power, creating high carbon emissions and energy use. Research focuses on making data centers more energy efficient—they use a large share of global power—by using efficient hardware, smart power management, and renewable energy like solar and wind. These changes cut carbon emissions and help organizations meet sustainability goals.
Making AI models use less energy is another key research area. Machine learning algorithms that need less computational power make AI more energy efficient without losing performance. Techniques like model pruning and quantization save significant energy during training and inference. This conserves energy, extends battery life in embedded systems, and reduces AI workloads' environmental impact.
Edge computing offers a smart solution for better energy efficiency and lower environmental impact from centralized data processing. Processing data closer to its source means less need to send large amounts of information to distant data centers. This cuts power use and carbon emissions. The approach also conserves energy and tackles electronic waste by making better use of existing hardware.
Research also examines AI infrastructure's broader environmental and social effects. This includes studying environmental damage from extracting rare earth minerals for GPU hardware and AI's potential to worsen climate change without responsible management. By investigating these challenges, researchers help shape responsible development practices that put environmental sustainability first.
From sustainability to responsibility
The neocloud isn’t just an environmental statement. It’s a responsibility model. Every design choice — from transparent pricing to distributed compute — reflects a commitment to balance power with accountability. Adopting green cloud practices can also strengthen a company’s brand image and help meet environmental regulations and ESG goals. The AI Act, for instance, includes obligations for high-impact AI models to report energy efficiency, ensuring accountability in energy consumption. This regulatory framework underscores the importance of aligning AI development with sustainability goals, as it represents an EU effort to regulate AI systems based on their potential risks and impacts. Governments have also been pivotal in implementing regulations that promote green computing, such as the Energy Star program, which enhances the energy efficiency of IT products and contributes to broader environmental goals. Companies like Google have made recommendations to reduce energy use significantly in AI development, showcasing how industry leaders can drive progress in sustainable practices. Computer science plays a crucial role in developing responsible and sustainable AI infrastructure, driving innovations that make compliance and energy efficiency possible.
Compute with Hivenet proves that ethical and efficient infrastructure can coexist. It’s not about saving the planet with slogans; it’s about running the cloud responsibly.
Looking ahead, future trends in sustainable AI and cloud computing will continue to shape best practices and regulatory frameworks, ensuring ongoing progress toward greener, more responsible technology.
To explore the economic side of this model, see The economics of the neocloud, which explains how transparency and cost-efficiency align with sustainability.
The takeaway
Sustainability in the neocloud era isn’t a side benefit — it’s the core. Compute with Hivenet leads this shift by showing that performance, fairness, and environmental care can grow together.
Sustainable GPU cloud solutions are continually evolving to address new environmental and technological challenges, driving innovation in green computing.
A greener GPU cloud is possible. The neocloud is how we build it.
To continue the series, read The future of cloud sovereignty — why the neocloud matters for Europe — an exploration of how distributed compute empowers digital independence.
Frequently Asked Questions (FAQ)
How does Compute with Hivenet reduce greenhouse gas emissions?
By connecting existing hardware into a distributed GPU network that uses energy more efficiently and avoids new construction.
Is distributed compute reliable for large workloads?
Yes. Hivenet’s architecture manages resource allocation to maintain performance while lowering waste.
What makes Compute with Hivenet more sustainable than hyperscalers?
It reuses existing devices and runs them closer to users, minimizing energy use for cooling and transfer.
Does sustainability affect cost?
In this case, it reduces it. Efficient resource use means lower prices and fairer access for users.
Can sustainability and performance coexist?
Yes. Compute with Hivenet proves that green AI infrastructure can deliver high-speed performance without compromising environmental goals.
