AI Chip Race Heats Up: Samsung Funds Euclyd to Challenge Nvidia Dominance
September 16, 2026, 3:38 am
Location: Netherlands, North Holland, Amsterdam
Employees: 11-50
Founded date: 2017
Location: South Korea
Employees: 10001+
Founded date: 1938
Total raised: $6.4B
Samsung invests $231 million in Dutch AI chip startup Euclyd. This significant backing targets Nvidia's powerful grip on AI computing. Euclyd develops specialized silicon systems. They promise drastically lower energy use and reduced costs for AI inference workloads. Their novel chip and memory architecture offers a critical alternative to traditional GPUs. This strategic move highlights an industry-wide push for more efficient, sustainable AI infrastructure beyond current solutions. The global technology sector seeks to diversify its AI hardware supply chain.
The artificial intelligence hardware landscape faces a fundamental shift. Samsung, a global tech giant, is investing heavily. Its target: a Dutch startup named Euclyd. Euclyd secured $231 million in Series A funding. Samsung co-led this significant investment round. The goal is clear: challenge Nvidia's near-monopoly in AI chips. This move signals a broader industry pursuit of AI processing alternatives.
Nvidia commands the AI chip market. Its Graphics Processing Units (GPUs) power most generative AI applications. These chips, originally for gaming, now fuel AI training and inference. Nvidia's dominance has led to high costs and energy demands. Data centers consume vast amounts of electricity. This unsustainable trend forces innovation. Tech companies seek new solutions.
Euclyd offers a distinct approach. The startup focuses on AI inference. Inference involves running trained AI models. It processes requests and generates responses. Euclyd's systems feature an alternative processor. They also employ a unique memory architecture. This design differs fundamentally from traditional GPUs. It aims to boost efficiency. It also seeks to reduce energy consumption. Lowering costs for AI data centers is a core objective.
The company plans a comprehensive solution. It combines custom processors with advanced memory. Its data center systems are purpose-built. This integrated approach promises greater efficiency. It targets a lower cost per AI token. Widespread access to advanced AI becomes more feasible. This vision resonates with industry needs.
Samsung’s involvement extends beyond capital. The Korean electronics giant offers invaluable expertise. Samsung is a leading memory manufacturer. Memory bandwidth is crucial for AI workloads. Efficient chip packaging matters immensely. These factors determine processing efficiency. Samsung brings deep knowledge in these areas. Its vast supply chain network is also a major asset. This partnership provides Euclyd with strategic leverage. It accelerates their development timeline.
The push for AI chip alternatives is gaining momentum. Euclyd is not alone in this endeavor. Major tech hyperscalers are developing their own silicon. OpenAI revealed its "Jalapeño" AI chip. They claim industry-leading speed and efficiency. Google, Amazon Web Services (AWS), and Meta pursue similar goals. Each seeks to optimize AI workloads for their specific needs. They want to reduce reliance on external suppliers. This internal development trend validates Euclyd's mission.
Euclyd's business model is two-pronged. First, they plan to sell server racks. These physical systems target enterprises. Companies can run AI inference securely on-premises. This offers control and data privacy. Second, Euclyd will license its chip designs. Other companies can integrate their intellectual property. This allows for custom silicon development. Both channels aim to democratize access to efficient AI hardware.
The startup faces significant challenges. Its systems are not yet proven at commercial scale. Nvidia holds a well-established market position. Euclyd plans a deliberate rollout. Physical chip systems are slated for 2028. The company hopes to serve thousands of enterprise customers by 2030. This timeline requires sustained innovation and execution. Peter Wennink, former ASML CEO, now chairs Euclyd's board. His experience provides critical guidance.
Euclyd's vision aligns with critical global priorities. AI's potential is enormous. It drives economic growth. It fuels scientific discovery. It enhances national competitiveness. However, its infrastructure must evolve. Current systems are becoming bottlenecks. High energy demands limit widespread adoption. Euclyd addresses this core limitation. Their innovative architecture promises a more sustainable AI future.
The investment reflects a significant industry bet. Investors believe in Euclyd’s thesis. The next chapter of AI infrastructure may not solely rely on GPUs. New specialized hardware is emerging. These solutions address specific AI challenges. Efficiency and cost reduction are paramount. Samsung's backing strengthens Euclyd's position. It provides crucial resources for testing and deployment. This funding fuels the ongoing AI hardware revolution. The future of AI computing hangs in the balance.
The artificial intelligence hardware landscape faces a fundamental shift. Samsung, a global tech giant, is investing heavily. Its target: a Dutch startup named Euclyd. Euclyd secured $231 million in Series A funding. Samsung co-led this significant investment round. The goal is clear: challenge Nvidia's near-monopoly in AI chips. This move signals a broader industry pursuit of AI processing alternatives.
Nvidia commands the AI chip market. Its Graphics Processing Units (GPUs) power most generative AI applications. These chips, originally for gaming, now fuel AI training and inference. Nvidia's dominance has led to high costs and energy demands. Data centers consume vast amounts of electricity. This unsustainable trend forces innovation. Tech companies seek new solutions.
Euclyd offers a distinct approach. The startup focuses on AI inference. Inference involves running trained AI models. It processes requests and generates responses. Euclyd's systems feature an alternative processor. They also employ a unique memory architecture. This design differs fundamentally from traditional GPUs. It aims to boost efficiency. It also seeks to reduce energy consumption. Lowering costs for AI data centers is a core objective.
The company plans a comprehensive solution. It combines custom processors with advanced memory. Its data center systems are purpose-built. This integrated approach promises greater efficiency. It targets a lower cost per AI token. Widespread access to advanced AI becomes more feasible. This vision resonates with industry needs.
Samsung’s involvement extends beyond capital. The Korean electronics giant offers invaluable expertise. Samsung is a leading memory manufacturer. Memory bandwidth is crucial for AI workloads. Efficient chip packaging matters immensely. These factors determine processing efficiency. Samsung brings deep knowledge in these areas. Its vast supply chain network is also a major asset. This partnership provides Euclyd with strategic leverage. It accelerates their development timeline.
The push for AI chip alternatives is gaining momentum. Euclyd is not alone in this endeavor. Major tech hyperscalers are developing their own silicon. OpenAI revealed its "Jalapeño" AI chip. They claim industry-leading speed and efficiency. Google, Amazon Web Services (AWS), and Meta pursue similar goals. Each seeks to optimize AI workloads for their specific needs. They want to reduce reliance on external suppliers. This internal development trend validates Euclyd's mission.
Euclyd's business model is two-pronged. First, they plan to sell server racks. These physical systems target enterprises. Companies can run AI inference securely on-premises. This offers control and data privacy. Second, Euclyd will license its chip designs. Other companies can integrate their intellectual property. This allows for custom silicon development. Both channels aim to democratize access to efficient AI hardware.
The startup faces significant challenges. Its systems are not yet proven at commercial scale. Nvidia holds a well-established market position. Euclyd plans a deliberate rollout. Physical chip systems are slated for 2028. The company hopes to serve thousands of enterprise customers by 2030. This timeline requires sustained innovation and execution. Peter Wennink, former ASML CEO, now chairs Euclyd's board. His experience provides critical guidance.
Euclyd's vision aligns with critical global priorities. AI's potential is enormous. It drives economic growth. It fuels scientific discovery. It enhances national competitiveness. However, its infrastructure must evolve. Current systems are becoming bottlenecks. High energy demands limit widespread adoption. Euclyd addresses this core limitation. Their innovative architecture promises a more sustainable AI future.
The investment reflects a significant industry bet. Investors believe in Euclyd’s thesis. The next chapter of AI infrastructure may not solely rely on GPUs. New specialized hardware is emerging. These solutions address specific AI challenges. Efficiency and cost reduction are paramount. Samsung's backing strengthens Euclyd's position. It provides crucial resources for testing and deployment. This funding fuels the ongoing AI hardware revolution. The future of AI computing hangs in the balance.