Dutch startup Euclyd, founded in 2024, has secured a $231 million funding round to develop an AI inference chip system designed to rival Nvidia's market-leading GPUs. The round was co-led by Samsung, Somerset Capital Partners, the Scaleup Europe Fund managed by EQT, and Innovation Industries, marking significant backing from both industry and venture capital sources [1]. Euclyd CEO Bernardo Kastrup stated that the company's Series A drew support from these investors, with Samsung's involvement extending beyond financial backing due to its expertise in memory manufacturing, engineering, supply chain, and global network [1].
Euclyd's chip system features a distinct architecture from traditional GPUs, targeting AI inference workloads and aiming to reduce energy consumption and costs in AI data center infrastructure. The company plans to generate revenue through hardware sales and licensing its intellectual property to firms seeking to develop their own chips [1]. While Euclyd's systems have not yet been proven at scale in commercial deployments, Kastrup emphasized the need to fundamentally change the infrastructure supporting AI to unlock its full economic and scientific potential [1].
The funding round comes amid a surge in investment for AI chip alternatives, as Nvidia maintains a near-monopoly in high-end AI chips. Major tech companies such as OpenAI, Google, AWS, and Meta are also developing proprietary AI chips, with OpenAI recently announcing its Jalapeño chip featuring 'industry-leading speed and efficiency' [1].
Euclyd aims to begin rolling out its physical chip systems in 2028, targeting thousands of enterprise customers by 2030. The company is positioning itself as a key player in the evolving AI hardware landscape, seeking to provide secure, self-hosted inference solutions and foundational model infrastructure [1].
CONCLUSION
Euclyd's $231 million funding round, led by Samsung and other major investors, signals strong momentum for AI chip alternatives to Nvidia. While Euclyd's technology is not yet commercially proven, its ambitious rollout plans and industry partnerships suggest potential disruption in the AI hardware market. The investment highlights growing demand for energy-efficient, scalable AI infrastructure and intensifies competition among chipmakers.