Pioneering the Green Wave of Adaptive AI for Sustainable Business Growth

In the era of digital transformation, artificial intelligence (AI) stands as a powerful force, reshaping the landscape of business by optimizing processes and elevating decision-making. Celebrated as a growth engine, AI’s impact on business efficiency is undeniable. However, as industries increasingly harness the computational prowess of advanced language models (LLMs), such as LLMs, a looming challenge surfaces — the substantial carbon footprint inherent in the training and runtime demands of these sophisticated AI systems. [1]

Training LLMs demands extensive computational resources, contributing to their hefty price tags. The resource hunger persists at runtime, necessitating power-hungry GPUs, specialized accelerator hardware that can rival or surpass the energy consumption of an average personal computer.

The Transformative Role of AI in Business

AI is now indispensable, driving automation and process optimization, cost reduction and heightened efficiency across industries. Despite the efficiency gains (that can translate to energy savings), discussions of the environmental costs and benefits associated with AI gained momentum. These discussions underscore the staggering training efforts required, with models like GPT-3 demanding a whopping 1,287 MWh, Gopher consuming 1,066 MWh, OPT utilizing 324 MWh, and BLOOM requiring 433 MWh [2]. In comparison, on average a US household consumes 11MWh of electricity each year,a German household 3.3MWh [3]. As the demand for AI continues to grow, addressing its carbon footprint becomes imperative for sustainable business practices.

The argument that the emissions incurred during LLM training will be offset by future emission reductions is a complex one. While LLMs indeed have the potential to optimize processes and drive efficiencies, we need to acknowledge that the upfront environmental cost is substantial. Balancing immediate gains with long-term benefits requires a nuanced approach — one that prioritizes sustainable AI solutions to ensure a net positive impact on the environment throughout their lifecycle. The quest for efficiency must be tempered by a commitment to mitigating the ecological impact of AI systems, prompting a paradigm shift towards environmentally conscious AI solutions.

semantha: The Smart & Green AI

In the realm of “modern” AI solutions, many are tethered to the use of Large Language Models (LLMs), even for tasks beyond their original design. These solutions not only rely on GPUs during training but also necessitate them at runtime. Inference on such expansive models is often sluggish without GPU support, presenting challenges for those seeking to self-host AI solutions. The absence of a robust GPU infrastructure can impact data governance, privacy, and overall feasibility, restricting the on-premises deployment of AI capabilities.

In developing semantha, an innovative AI solution tailored for document processing—whether in texts, speech, or videos—we prioritized both intelligence and sustainability. semantha distinguishes itself by minimizing CO2 output through CPU optimization, eliminating the need for resource-intensive deployment on specialized hardware. This not only positions semantha as an eco-friendly choice but also as a cost-effective solution for on-premises deployment.

Unlike many modern AI solutions reliant on power-hungy GPUs, semantha selectively employs LLMsfor specific tasks within semantha’s extensive repertoire. Importantly, LLM usage occurs on-demand after meticulous pre-processing of documents and queries. By diminishing reliance on power-hungry GPUs, optimizing for CPUs, and avoiding costly re-training on customer data, semantha contributes to ecological sustainability, resource savings, and reduced operational costs.

semantha’s impact isn’t confined to technological efficiency alone; it addresses the carbon footprint of the human workforce as well (see also our recently updated blog post, “Your AI’s Carbon Footprint”). The success story of Forvia (formerly Hella) exemplifies semantha’s flexibility. Within Forvia, semantha analyzes incoming requirements and connects the dots between inbound documents and Forvia’s extensive knowledge base, facilitating a streamlined process for expert evaluation. Also, semantha prepares contract reviews, seamlessly integrated into Forvia’s existing IT landscape.

Importantly, semantha is not confined to a singular role; it is a versatile solution with broad applications: From automotive to reinsurance companies – from requirements analysis to helping in sustainability (reporting) efforts, semantha’s adaptability positions it as a strategic choice for industries seeking to balance innovation with environmental responsibility. The success exemplified by Forvia underscores semantha’s ability to not only meet but exceed the diverse needs of organizations across the spectrum.

The Future of AI must be Green

As business leaders increasingly recognize the imperative of environmentally friendly practices, semantha emerges as a beacon of green innovation. Its minimal environmental impact, emphasis on CPU computation, and adaptability make it an ideal building block for transformative AI projects. In navigating the future AI landscape, decision-makers must carefully weigh the environmental ramifications of their choices.

We advocate for a paradigm shift towards sustainability in AI initiatives. Rather than traditional approaches involving multiple specialized models, subject to constant retraining in the face of evolving realities, semantha offers a versatile solution, promising a higher return on investment and contributing significantly to sustainability efforts.

As we embrace this paradigm shift, semantha stands ready to integrate seamlessly into diverse process landscapes. For those eager to explore how semantha can enhance their sustainability initiatives and operational efficiency, we invite you to get in touch with us. Together, let’s pave the way for an AI future that is not only intelligent but inherently green.

1. Lacoste, Alexandre & Luccioni, Alexandra & Schmidt, Victor & Dandres, Thomas. (2019). Quantifying the Carbon Emissions of Machine Learning. (arXiv preprint arXiv:1910.09700) https://mlco2.github.io/impact/ 
2. Alexandra Sasha Luccioni, Sylvain Viguier, Anne-Laure Ligozat: Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model. Journal of Machine Learning Research 24 (2023) 1-15, https://jmlr.org/papers/volume24/23-0069/23-0069.pdf 
3. For details see Electricity use in homes – U.S. Energy Information Administration (EIA), Private Households – German Federal Statistical Office (destatis.de). While we are sure there is a difference in the statistical methodology, a large part of the difference stems from the comparably widespread use of air conditioning in the US.