Google’s Gemini AI: Each Prompt Drinks Your Water And Devours Your Electricity

A powerhouse in the AI world, Google’s Gemini represents a major leap in multimodal artificial intelligence technology. The system processes text, images, audio, and video in any order with its decoder-only transformer architecture. Gemini comes in several sizes, from the smaller Nano-1 with 1.8 billion parameters to much larger Ultra versions. The latest models can handle up to 10 million tokens of context, allowing for massive amounts of information to be processed at once.

Google’s custom AI accelerators power Gemini’s operations. These include TPU v4, v5e, and the latest v5p chips, designed specifically for fast and cost-efficient AI processing. Gemini 2.0 Flash runs twice as fast as the earlier 1.5 Pro model while maintaining high performance on key benchmarks. This speed and efficiency are essential as Gemini serves billions of users across Google’s product ecosystem. The model was created as a direct response to ChatGPT, highlighting the competitive nature of AI development. Gemini Ultra demonstrates superior performance on the MMLU benchmark, achieving an impressive 90.0% score that surpasses human experts.

The multimodal capabilities of Gemini are impressive. It can process thousands of images in a single request – up to 3,000 with Gemini 1.5 Pro and 3,600 with newer versions. The system supports numerous image formats and can generate both text and images as outputs. The Multimodal Live API enables real-time streaming of audio and video inputs.

Gemini’s reach extends throughout Google’s services. It’s embedded in Search, YouTube, Gmail, Maps, Play, and Android. Developers access its power through Vertex AI and the Gemini API. Project Astra uses Gemini for advanced tool integration and agentic applications that can perform complex tasks. The computational demands of these widespread applications contribute to the energy consumption crisis that could see AI accounting for nearly 19% of power demand by 2028.

The model has evolved quickly since its initial release. Starting as a successor to LaMDA and PaLM 2, Gemini has progressed through versions 1.0, 1.5, 2.0, and 2.5, each bringing improvements in speed, context handling, and multimodal capabilities. The latest versions excel at object detection and segmentation tasks that previously required specialized machine learning models.

As powerful as Gemini is, its operation comes with significant resource requirements. Each prompt processed by the system consumes computational resources, contributing to water usage for cooling and electricity consumption in data centers.