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IT之家 科技新闻· · Original publication time AI score33

Intel forms K-AI Full Stack Alliance with 23 Korean companies and shows D270 chipMachine translation

英特尔联合 23 家韩国企业成立 K-AI 全栈联盟,并展示 D270 芯片

AI-assisted summary

Intel announced the K-AI Full Stack Alliance with 23 Korean companies to collaborate on hardware, software and industry-specific AI solutions for AI infrastructure in Korea. It also showed the D270 data-center inference accelerator. The performance figures came from Intel's presentation; no availability date was given.

Article · AI translation

IT Home reported on November 14 that, according to South Korean media outlet The Elec, Intel announced the formal establishment of the “K-AI Full-Stack Alliance” with 23 South Korean companies at the “2026 Intel AI Summit” held at COEX in Seoul on November 12.

The K-AI Full-Stack Alliance is a broad industry consortium centered on Intel and comprising multiple South Korean AI companies. It aims to build a comprehensive AI ecosystem in South Korea covering the entire technology stack. Alliance members will cooperate across the industry chain, including hardware, software, industry-specific AI solutions and services, with the aim of jointly building an ecosystem for sovereign AI and open AI infrastructure for the South Korean market.

IT Home learned that the first 23 companies to join include South Korean server manufacturers such as KTNF, AI chip and NPU design startups such as FuriosaAI and DEEPX, AI large-model developers such as Upstage, and cloud computing and software service providers such as MegazoneCloud and Hancom.

In addition, Intel publicly displayed a physical D270 chip from its next-generation data center inference accelerator product line, the Intel GPU D series, at the event.

According to official data, the product can deliver up to 26,200 TFLOPS of FP4 computing performance at a power consumption of 350 watts. Compared with its competitor, Nvidia’s RTX 6000 Pro, Intel demonstrated several performance advantages, including 30% lower latency when running the Gemma model, a 2.2-fold improvement in AI agent performance, and up to a 5.9-fold improvement in computing performance in financial simulations.

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