Researchers on the Korea Superior Institute of Science and Expertise (KAIST) have developed energy-efficient NPU expertise that demonstrates substantial efficiency enhancements in laboratory testing.
Their specialised AI chip ran AI fashions 60% sooner whereas utilizing 44% much less electrical energy than the graphics playing cards presently powering most AI methods, based mostly on outcomes from managed experiments.
To place it merely, the analysis, led by Professor Jongse Park from KAIST’s College of Computing in collaboration with HyperAccel Inc., addresses probably the most urgent challenges in fashionable AI infrastructure: the large vitality and {hardware} necessities of large-scale generative AI fashions.
Present methods reminiscent of OpenAI’s ChatGPT-4 and Google’s Gemini 2.5 demand not solely excessive reminiscence bandwidth but in addition substantial reminiscence capability, driving corporations like Microsoft and Google to buy a whole bunch of 1000’s of NVIDIA GPUs.
The reminiscence bottleneck problem
The core innovation lies within the staff’s strategy to fixing reminiscence bottleneck points that plague current AI infrastructure. Their energy-efficient NPU expertise focuses on “light-weight” the inference course of whereas minimising accuracy loss—a essential steadiness that has confirmed difficult for earlier options.
PhD scholar Minsu Kim and Dr Seongmin Hong from HyperAccel Inc., serving as co-first authors, introduced their findings on the 2025 Worldwide Symposium on Laptop Structure (ISCA 2025) in Tokyo. The analysis paper, titled “Oaken: Quick and Environment friendly LLM Serving with On-line-Offline Hybrid KV Cache Quantization,” particulars their complete strategy to the issue.
The expertise centres on KV cache quantisation, which the researchers establish as accounting for most reminiscence utilization in generative AI methods. By optimising this element, the staff allows the identical stage of AI infrastructure efficiency utilizing fewer NPU gadgets in comparison with conventional GPU-based methods.
Technical innovation and structure
The KAIST staff’s energy-efficient NPU expertise employs a three-pronged quantisation algorithm: threshold-based online-offline hybrid quantisation, group-shift quantisation, and fused dense-and-sparse encoding. This strategy permits the system to combine with current reminiscence interfaces with out requiring modifications to operational logic in present NPU architectures.
The {hardware} structure incorporates page-level reminiscence administration strategies for environment friendly utilisation of restricted reminiscence bandwidth and capability. Moreover, the staff launched new encoding strategies particularly optimised for quantised KV cache, addressing the distinctive necessities of their strategy.
“This analysis, by way of joint work with HyperAccel Inc., discovered an answer in generative AI inference light-weighting algorithms and succeeded in growing a core NPU expertise that may resolve the reminiscence downside,” Professor Park defined.
“By way of this expertise, we applied an NPU with over 60% improved efficiency in comparison with the newest GPUs by combining quantisation strategies that cut back reminiscence necessities whereas sustaining inference accuracy.”
Sustainability implications
The environmental affect of AI infrastructure has change into a rising concern as generative AI adoption accelerates. The energy-efficient NPU expertise developed by KAIST presents a possible path towards extra sustainable AI operations.
With 44% decrease energy consumption in comparison with present GPU options, widespread adoption might considerably cut back the carbon footprint of AI cloud providers. Nonetheless, the expertise’s real-world affect will depend upon a number of components, together with manufacturing scalability, cost-effectiveness, and business adoption charges.
The researchers acknowledge that their resolution represents a major step ahead, however widespread implementation would require continued improvement and business collaboration.
Business context and future outlook
The timing of this energy-efficient NPU expertise breakthrough is especially related as AI corporations face rising strain to steadiness efficiency with sustainability. The present GPU-dominated market has created provide chain constraints and elevated prices, making different options more and more enticing.
Professor Park famous that the expertise “has demonstrated the potential of implementing high-performance, low-power infrastructure specialised for generative AI, and is anticipated to play a key function not solely in AI cloud knowledge centres but in addition within the AI transformation (AX) atmosphere represented by dynamic, executable AI reminiscent of agentic AI.”
The analysis represents a major step towards extra sustainable AI infrastructure, however its final affect can be decided by how successfully it may be scaled and deployed in business environments. Because the AI business continues to grapple with vitality consumption issues, improvements like KAIST’s energy-efficient NPU expertise provide hope for a extra sustainable future in synthetic intelligence computing.
(Photograph by Korea Superior Institute of Science and Expertise)
See additionally: The 6 practices that guarantee extra sustainable knowledge centre operations
Wish to study extra about cybersecurity and the cloud from business leaders? Try Cyber Safety & Cloud Expo happening in Amsterdam, California, and London.
Discover different upcoming enterprise expertise occasions and webinars powered by TechForge right here.
