A new high-tech solar-powered streetlight, iLamp, made by British greentech firm Conflow Power Group, provides a unique solution to a growing global AI power consumption crisis – reducing the carbon footprint to near-zero while supplying the world’s first autonomous, off-grid, distributed AI data centre.
Electricity consumption from AI data centres is estimated to be 415 terawatt-hours (TWh) and this is set to more than double by 2030 to around 945 TWh, according to the International Energy Agency, leading several nations to expand their nuclear power station programmes to cope. A recent Cornell University study also found that by 2030, the current rate of AI growth in the US will add up to 44 million tons of carbon dioxide to the atmosphere – equivalent to adding 10 million petrol cars to the road.
Meanwhile, surging AI demand could consume 1.7 trillion gallons of fresh water globally by 2027 for cooling, according to researchers at the University of California, Riverside.
The iLamp solves these problems. Each unit is powered off-grid using low-maintenance, self-cleaning cylindrical solar panels housed in borosilicate glass for durability and high transmission.
The lamp generates up to 200–600W of power, depending on configuration, while only consuming 80W. This allows the lamp to host embedded Nvidia Jetson Edge AI modules that deliver up to 67 trillion operations per second (TOPS) while drawing just 15W per unit. Higher-performance versions are designed to support Nvidia’s next-generation compact AI accelerators, providing on-device inference for models running into the hundreds of billions of parameters.
Edward Fitzpatrick, Director of Conflow Power, which has a £400 million balance sheet, said: “While tech giants scramble to build nuclear power stations to feed their AI addiction, we’ve built something smarter. Right now, in order to power AI, the likes of OpenAI or Google Gemini need a huge building full of GPUs and they have to pump massive amounts of electricity into it, along with a huge water supply for the cooling system. This is inefficient and we need a smart solution.
“There are streetlights everywhere in our towns and cities. By replacing them with iLamps fitted with Nvidia Jetson processors, you create a huge distributed data centre which is clean, non-water-hungry and low latency because the servers are near to the users. We are already in advanced negotiations with several large companies and world governments to make this a reality.”
The iLamps come with a 20-year warranty and the base model costs £7,500, but AI providers pay to use the embedded compute – meaning buyers can earn revenue instead of incurring running costs. Units can also be fitted with complementary technologies including AI cameras, sensors, Wi-Fi, private connectivity infrastructure and other edge hardware.
Conflow sells exclusive territorial licences to operate iLamp in different global territories. Licensees own the local company and sell the product under licence. Customers typically get local and federal tax incentives and rebates, and AI providers pay to use the compute power. Customers generate revenue instead of paying electricity bills.
In one transaction last year, Conflow Power Group sold an exclusive licence to iLamp Florida LLC for US$45 million, which later sold a sub-sector licence to iLamp Secure Inc. for US$80 million in a 50-year deal to deliver advanced safety infrastructure to 4,400 schools across Florida. This resulted in an addressable market valuation of US$777 million.
The iLamps supplied under this programme are fitted with an integrated AI safety stack including weapon identification, gunshot detection, next-generation Automatic License Plate Recognition, facial recognition, early fire detection, smoke warnings, vehicle speed detection and private wireless connectivity.
Another version of the iLamp is being developed in collaboration with a major tech firm to equip rugby clubs in the UK and France with AI-assisted video analytics for tactical decision-making, along with lighting for night training and match analysis.
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