Revolutionary NPU technology: 60% faster AI with 44% less energy!

Revolutionary NPU technology: 60% faster AI with 44% less energy!

What's in the world of artificial intelligence? At an exciting point of the technology turnaround are the researchers at the Korea Advanced Institute of Science and Technology (Kaist). They have developed a new, energy-efficient NPU technology that increases the performance of generative AI models by impressive 60%, while energy consumption is reduced by 44%. [Cloudcomputing-news.net] reports on the progress that was achieved as part of this research led by Professor Jongseok Park, in collaboration with Hyperaccel Inc..

Current large AI models such as Openais Chatgpt-4 and Google Gemini 2.5 are true arithmetic monsters that require a high storage capacity and bandwidth. It is no wonder that companies like Microsoft and Google buy hundreds of thousands from Nvidia GPUs to meet these requirements. But that could change, because the new NPU technology aims to solve existing bottlenecks in the AI infrastructure and keeps the energy consumption in chess.

progress through specialized hardware

In the core of this innovation, the optimization of storage management is through the use of a specialized hardware architecture. Minsu Kim and Dr. Seongmin Hong from Hyperaccel Inc. worked together on research, the results of which were presented in Tokyo on the International Symposium on Computer Architecture (ISCA 2025). The research work is entitled "Oaken: Fast and Efficient LLM Serving with online offline Hybrid KV Cache Quantization" and focuses heavily on the KV-Cache quantization, which constitutes an enormous part of memory consumption in generative AI systems. With this technology, the same performance can be achieved with fewer NPU devices, which not only lowers costs, but also protects the environment.

By implementing a three -track quantization algorithm, engineers can minimally keep the loss of accuracy during the inference. This includes combative strategies such as threshold-based online offline hybrid quantization, group shifting quantization and a merged density and sparse coding. These approaches are designed to optimally deal with the limited range and capacity of the current systems and thus to promote the longevity of the AI infrastructure.

a step into the sustainable future

energy efficiency in the AI cannot be done with a single solution. Overall, research represents a significant progress in the direction of sustainable AI infrastructure, the effects of which are only correctly developed with the scaling and implementation in commercial environments. The CO2 footprint of AI-Cloud services could significantly reduce the new developments. The current research on other techniques also plays a role here that aim to reduce energy consumption, such as model circumcision, quantization or efficient architectures. Scientists and companies are required to bring these goals into harmony with increasing demands on performance.

But what is the key to the future of AI energy efficiency? According to [FOCALX.AI], not only innovative hardware and specialized algorithms are required, but also increased cooperation between technicians and companies in order to create sustainable solutions. The challenges are diverse: from the balance between performance and efficiency to possible hardware restrictions.

Overall, we are faced with an exciting development that could show us how powerful, energy -efficient infrastructures can be implemented in the AI. It remains to be seen which impulses this new NPU technology will set in the industry and how it can help reduce the ecological footprints of the companies.

we keep in mind the developments - the future sounds promising!