Samsung backs Euclyd in $230 million push for Nvidia alternatives

Highlights

Euclyd raised €200 million in Series A funding.
Samsung participated as a co-lead investor.
Commercial systems are targeted for 2028.
Euclyd is focused on AI inference hardware.

The Series A was co-led by Samsung, Somerset Capital Partners, the Scaleup Europe Fund managed by EQT, and Innovation Industries, CNBC reported. Founded in 2024, Euclyd is designing an integrated AI computing system covering both processor and memory architecture, with commercial hardware expected to begin rolling out in 2028.

Euclyd targets the AI inference market

Nvidia dominates the market for high-end AI processors after GPUs, originally developed for graphics, became central to training and running large AI models. That position has prompted hyperscalers and semiconductor startups to develop specialized processors that could lower costs or improve efficiency for specific workloads.

Euclyd is concentrating on inference, the process of running trained AI models to produce results. Its architecture differs from conventional GPUs and is intended to reduce the energy consumption and infrastructure costs associated with increasingly demanding AI workloads.

The company plans two primary businesses. It intends to sell complete hardware and rack systems to enterprises seeking privately hosted AI inference, while also licensing its intellectual property to companies developing custom processors.

Samsung brings semiconductor expertise

Samsung's participation gives Euclyd access to more than capital. As one of the world's largest memory-chip manufacturers, Samsung brings experience in memory technology, engineering, semiconductor supply chains, and large-scale production, areas that could become important as Euclyd moves from design toward physical systems.

The investment comes as competition in AI processors broadens. Google, Amazon Web Services, and Meta are developing proprietary AI silicon, while OpenAI announced its first AI chip, Jalapeño, in August. These projects reflect growing demand for alternatives as AI infrastructure spending expands.

Euclyd, however, remains at an early stage. Its systems have not yet demonstrated performance or efficiency at scale in commercial deployments, leaving execution as a central challenge.

AI chip competition moves beyond GPUs

The €200 million round highlights how capital is moving toward new computing architectures as AI data centers consume more electricity and require increasingly expensive hardware. Nvidia remains the benchmark, but customers are exploring specialized chips that could offer better economics for inference.

Euclyd aims to deploy its first physical systems in 2028 and serve thousands of enterprise customers by 2030. Samsung's backing strengthens its route toward manufacturing and commercialization, but the company's ability to compete will ultimately depend on whether its architecture delivers measurable advantages at scale.

We have previously highlighted that OpenAI and Samsung deepen cooperation on next-generation chips.

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