Samsung Co-Leads $231M Series A for Dutch AI Chip Startup Euclyd
Dutch semiconductor startup Euclyd has secured €200 million ($231 million) in Series A funding to develop specialized hardware aimed at challenging Nvidia's dominance in AI inference. The financing round was co-led by tech giant Samsung alongside Somerset Capital Partners, Innovation Industries, and the Scaleup Europe Fund managed by EQT.
Key points
- Euclyd raised a €200 million ($231 million) Series A co-led by Samsung, Somerset Capital Partners, EQT's Scaleup Europe Fund, and Innovation Industries.
- The startup is designing specialized chip and memory architectures specifically optimized to cut energy costs during AI inference workloads.
- Physical hardware deployments are scheduled to begin in 2028, alongside plans to license chip intellectual property to third parties.
Founded in 2024, Euclyd is building a novel processor and memory architecture specifically optimized for foundational AI model inference rather than general-purpose graphics processing. The company plans to commercialize its technology by selling physical rack systems directly to enterprises requiring secure, on-premises AI capabilities, as well as licensing its intellectual property to external chip designers.
Beyond capital, Samsung brings critical memory manufacturing expertise, engineering capacity, and supply chain leverage to the startup. Euclyd aims to initiate commercial hardware deployments by 2028, targeting thousands of enterprise data centers by 2030 as hyperscalers and startups aggressively seek energy-efficient alternatives to traditional GPUs.
The story so far
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Samsung backs Nvidia AI chip rival in $230 million funding round as GPU alternatives boom
CNBC Technology · 14 September 2026
Samsung has backed a Dutch AI chipmaker in a $231 million funding round, amid a boom in investment in alternatives to Nvidia's graphic processing units.
Euclyd CEO Bernardo Kastrup told CNBC exclusively that the company's 200-million-euro Series A drew backing from Somerset Capital Partners, the Scaleup Europe Fund managed by EQT and Innovation Industries, which all co-led the round alongside Samsung.
The startup, founded in 2024, is designing an AI chip system, with different architecture to GPUs, for inference**,** including the processor and memory architecture.
Nvidia became the world's most valuable company after its GPUs, originally designed for gaming, were repurposed for training and using AI models, known as inference.
The company has developed a near monopoly over the market for the highest-end chips, but some hyperscalers, alongside startups, are looking to develop their own processors for AI workloads.
OpenAI announced in August that its first AI chip, the Jalapeño, had "industry-leading speed and efficiency." Google, AWS and Meta are all also developing their own AI chips.
"AI is becoming a foundation of economic growth, scientific discovery and national competitiveness, but its potential will remain constrained unless we fundamentally change the infrastructure beneath it," said Kastrup.
Euclyd's systems have yet to be proven at scale in commercial deployments.
But the company said its silicon systems for foundational models will reduce the energy needs and costs of AI data center infrastructure, which is seeing a funding boom.
The company is targeting two revenue streams: selling hardware and physical rack systems to enterprise customers who want secure self-hosted AI inference, and selling its intellectual property to companies that want to develop their own chips based on pre-existing tech.
"Samsung can help us in more ways than money," said Kastrup. "They are one of the biggest memory manufacturers in the world. They do a lot of engineering, they know a lot about systems, they know the supply chain, they have a huge network."
Euclyd aims to begin rolling out its physical chip systems in 2028, with thousands of enterprise customers being served by 2030, Kastrup told CNBC.
This text was published by CNBC Technology and written by Kai Nicol-Schwarz. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗
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