Samsung has co-led a $231 million Series A funding round for Dutch AI chip startup Euclyd, signaling a growing challenge to Nvidia's GPU dominance in the AI market. The round, which also includes Somerset Capital Partners, the Scaleup Europe Fund, and Innovation Industries, underscores the boom in investment for AI chip alternatives.
TL;DR
- Euclyd raises $231M in Series A funding co-led by Samsung, aiming to develop AI chips with a different architecture to GPUs.
- The startup plans to target enterprise customers with both hardware sales and IP licensing, with rollout beginning in 2028.
- Investment in AI chip alternatives is booming as hyperscalers and startups seek to reduce reliance on Nvidia's GPUs.
What happened
Euclyd, a Dutch AI chipmaker founded in 2024, has raised $231 million in a Series A funding round. The round was co-led by Samsung, Somerset Capital Partners, the Scaleup Europe Fund managed by EQT, and Innovation Industries. According to CNBC, Euclyd's CEO Bernardo Kastrup confirmed the funding exclusively to the outlet.
The startup is designing an AI chip system with a different architecture to GPUs, focusing on inference. Euclyd aims to target two revenue streams: selling hardware and physical rack systems to enterprise customers and licensing its intellectual property to companies developing their own chips.
Euclyd plans to begin rolling out its physical chip systems in 2028, with the goal of serving thousands of enterprise customers by 2030. The company claims its systems will reduce the energy needs and costs of AI data center infrastructure.
Why it matters
The investment in Euclyd highlights the growing challenge to Nvidia's GPU dominance in the AI market. Nvidia's GPUs, originally designed for gaming, have become essential for training and using AI models, making the company the world's most valuable. However, hyperscalers and startups are increasingly looking to develop their own processors for AI workloads.
For developers and startups, the rise of AI chip alternatives could lead to more options for hardware and potentially lower costs. For investors, the boom in AI chip investment presents opportunities in a rapidly growing market. However, Euclyd's systems have yet to be proven at scale in commercial deployments, which is a key risk factor.
The competitive landscape for AI chips is heating up, with companies like OpenAI, Google, AWS, and Meta all developing their own AI chips. Euclyd's partnership with Samsung could give it an edge, as Samsung's expertise in memory manufacturing, engineering, and supply chain management could be valuable assets.
Key facts
- Euclyd raised $231 million in a Series A funding round co-led by Samsung, Somerset Capital Partners, the Scaleup Europe Fund, and Innovation Industries (CNBC).
- The startup was founded in 2024 and is designing an AI chip system with a different architecture to GPUs, focusing on inference.
- Euclyd plans to begin rolling out its physical chip systems in 2028, aiming to serve thousands of enterprise customers by 2030.
- The company claims its systems will reduce the energy needs and costs of AI data center infrastructure.
- Euclyd aims to target two revenue streams: selling hardware and physical rack systems to enterprise customers and licensing its intellectual property.
- Samsung's involvement in the funding round could provide Euclyd with valuable expertise in memory manufacturing, engineering, and supply chain management.
- Nvidia's GPUs have become essential for training and using AI models, making the company the world's most valuable.
- Hyperscalers and startups are increasingly looking to develop their own processors for AI workloads, challenging Nvidia's GPU dominance.
Context
The AI chip market is experiencing a boom in investment as companies seek to reduce their reliance on Nvidia's GPUs. This trend is driven by the growing demand for AI infrastructure and the need for more efficient and cost-effective solutions.
The development of AI chips is a key area of competition among tech giants. Companies like OpenAI, Google, AWS, and Meta are all investing heavily in developing their own AI chips, aiming to gain a competitive edge in the market.
The rise of AI chip alternatives could have significant implications for the AI industry. It could lead to more options for hardware, potentially lower costs, and increased innovation in AI infrastructure. However, the success of these alternatives will depend on their ability to prove themselves at scale in commercial deployments.
