# AI and energy: why computing power and power consumption [Skip to content](#lm-inhoud)Network/[NL](/en/ai-en-energie)EN[Hubhub.llmnet.nlCompare models on task, language, cost and license.](https://hub.llmnet.nl/en/)[Communitycommunity.llmnet.nlPrompt techniques, patterns and system prompts.](https://community.llmnet.nl/en/)[APIapi.llmnet.nlLLMs in production: rate limits, routing, structured output.](https://api.llmnet.nl/en/)[Consultancyconsultancy.llmnet.nlRolling out AI in an organization, pilot to production.](https://consultancy.llmnet.nl/en/)[Newsnieuws.llmnet.nlAI developments, explained for the Netherlands.](https://nieuws.llmnet.nl/en/)[Benchmarkbenchmark.llmnet.nlMeasure AI quality yourself, on your own tasks.](https://benchmark.llmnet.nl/en/)[Careersvacatures.llmnet.nlAI roles, salaries and career paths in the Netherlands.](https://vacatures.llmnet.nl/en/)[Learnleren.llmnet.nlAI concepts in plain language, beginner to builder.](https://leren.llmnet.nl/en/)[Guidegids.llmnet.nlRun AI privately on your own Mac, PC, NAS or home server.](https://gids.llmnet.nl/en/)[Directorydirectory.llmnet.nlMapping the AI ecosystem: tools, models, companies.](https://directory.llmnet.nl/en/)[Radarradar.llmnet.nlSignals from X, research and communities for indie developers.](https://radar.llmnet.nl/en/)[Appsapps.llmnet.nlReviews of AI apps and open-source repos, with tips for builders.](https://apps.llmnet.nl/en/)[llmnet.nl — main site](https://llmnet.nl/en/)[](https://x.com/intent/post?url=https%3A%2F%2Fnieuws.llmnet.nl%2Fen%2Fai-en-energie&text=AI%20and%20energy%3A%20why%20computing%20power%20and%20power%20consumption)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fnieuws.llmnet.nl%2Fen%2Fai-en-energie)[](https://www.reddit.com/submit?url=https%3A%2F%2Fnieuws.llmnet.nl%2Fen%2Fai-en-energie&title=AI%20and%20energy%3A%20why%20computing%20power%20and%20power%20consumption)[](#)[](https://x.com/intent/post?url=https%3A%2F%2Fnieuws.llmnet.nl%2Fen%2Fai-en-energie&text=AI%20and%20energy%3A%20why%20computing%20power%20and%20power%20consumption)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fnieuws.llmnet.nl%2Fen%2Fai-en-energie)[](https://www.reddit.com/submit?url=https%3A%2F%2Fnieuws.llmnet.nl%2Fen%2Fai-en-energie&title=AI%20and%20energy%3A%20why%20computing%20power%20and%20power%20consumption)[](#)By Ivo Donker — created with AI assistance (Claude & Gemini) · Last updated: July 27, 2026 [llmnet.nl | News](/) # AI and energy: why computing power and power consumption matter Published on July 25, 2026 • Reading time: 3 minutes The rapid rise of generative artificial intelligence comes with a tangible physical price: energy. While software previously ran relatively 'invisibly', the current generation of Large Language Models (LLMs) requires an industrial scale of power and cooling. But why is this hunger for electricity so great, and how is it developing? ## The two phases of AI energy consumption The energy demand of AI can be roughly split into two intensive processes: - Training phase: Building a new model requires months of calculations by thousands of specialized GPUs (graphics cards) running continuously at full capacity. - Inference (usage): Every time a user enters a prompt and the model generates text or images, it requires significantly more computing power than a traditional search query. An average AI search query is estimated to consume 10 times as much energy as a standard Google search [to be verified]. ## Efficiency trends: Hardware is catching up with software The sector is painfully aware of these scalability issues. There is currently a strong push for efficiency. On the software side, we see the rise of smaller, highly specialized models (Small Language Models) and optimized frameworks such as Retrieval-Augmented Generation (RAG), which retrieve specific data without requiring heavy computation from the entire model. On the hardware side, it is all about heat management and efficiency. Data centers are increasingly experimenting with liquid cooling. We also see that waste heat from these giant server farms is increasingly being recovered, for example via large industrial heat pumps that heat nearby neighborhoods. Further reading: Curious about how to run models efficiently yourself or how to build your own setup? Read our comprehensive [Guide to Local AI Hardware](https://gids.llmnet.nl/en/). ## What this means for the user and developer The dependence on power-hungry cloud infrastructure forces developers and entrepreneurs to look for alternatives. Central data centers are hitting the limits of the power grid (grid congestion). This opens the door for decentralized solutions. We see a cautious trend towards local High-Performance Computing (HPC) setups. By running these at home or locally in the office, whether or not combined with a home battery to absorb peaks in electricity prices (dynamic tariffs) or to efficiently use self-generated solar energy, AI computing power can be commercially leased or deployed locally. The model is thus slowly shifting from pure cloud dependency to a hybrid, more distributed ecosystem. The future of AI is determined not only by who writes the smartest model, but also by who manages the underlying energy infrastructure most intelligently. © 2026 llmnet.nl. All rights reserved.