This funding will accelerate Euclyd’s chip development, tapeout plans, and early customer engagements
Samsung has co-led a $230 million funding round for Dutch startup Euclyd, which is developing an AI inference chip designed to compete with Nvidia’s dominant graphics processing units. The investment, announced on September 14, 2026, reflects growing interest in alternatives to Nvidia’s AI hardware as demand for specialized chips surges. Somerset Capital Partners also co-led the round, signaling strong confidence in Euclyd’s technology and market potential. Euclyd’s chip focuses specifically on AI inference tasks, aiming to deliver higher efficiency and lower power consumption than general-purpose GPUs used widely in data centers today. The company argues that purpose-built inference processors can outperform Nvidia’s offerings in specific workloads, particularly for real-time AI applications like video analysis and recommendation systems.
This funding will accelerate Euclyd’s chip development, tapeout plans, and early customer engagements, positioning it to enter a market where Nvidia currently holds over 80% share. How Euclyd Plans to Challenge Nvidia’s Dominance Euclyd’s approach centers on optimizing its architecture for inference rather than training, allowing it to strip away unnecessary components found in GPUs and reduce latency. The startup claims its design achieves up to three times better performance per watt for certain AI models compared to existing solutions. Samsung’s involvement brings not only capital but also potential access to advanced manufacturing processes and supply chain expertise critical for scaling chip production.
Industry analysts note that while Nvidia remains entrenched
Industry analysts note that while Nvidia remains entrenched, the rising cost and power demands of AI workloads are creating openings for specialized alternatives. Can Startups Really Disrupt the AI Chip Market? Despite the dominance of established players, history shows that focused innovation can shift market dynamics, especially when addressing unmet needs in efficiency and scalability. Euclyd’s success will depend on securing design wins with major cloud providers and demonstrating real-world performance advantages at scale. The $230 million infusion provides a significant runway, but translating technical promise into commercial adoption remains the biggest hurdle. If successful, Euclyd could contribute to a more diverse AI hardware ecosystem, reducing reliance on any single vendor. Frequently Asked Questions What makes Euclyd’s chip different from Nvidia’s GPUs? Euclyd’s chip is purpose-built for AI inference only, eliminating graphics and training-related circuitry to improve efficiency and reduce power consumption for specific AI workloads.
Why is Samsung investing in an AI chip startup? Samsung seeks to diversify its semiconductor portfolio and gain strategic exposure to emerging AI hardware technologies that could complement its existing foundry and memory businesses. How soon could Euclyd’s chip reach the market? The company plans to use the funding to complete chip design and begin tapeout in late 2027, with initial samples expected for customer testing in 2028.