Core Viewpoint - The article discusses the advancements in AI chip technology, highlighting the launch of Hailo-10H by Hailo Technologies and the adoption of RNGD by FuriosaAI for LG's AI models, emphasizing their efficiency and performance in edge computing and AI applications [3][6]. Group 1: Hailo Technologies and Hailo-10H - Hailo Technologies launched its second-generation AI accelerator, Hailo-10H, which supports generative AI capabilities without relying on cloud connectivity [3][4]. - The Hailo-10H chip is designed for edge environments with a typical power consumption of only 2.5 watts, making it suitable for various applications from personal devices to automotive systems [4][5]. - The chip allows developers to run advanced visual and generative AI models directly on edge devices, enabling ultra-low latency real-time responses [4][5]. Group 2: FuriosaAI and RNGD - FuriosaAI secured a significant client in LG, which is utilizing its RNGD inference accelerator for powering servers running the Exaone series of large language models [6][7]. - The RNGD chip, while not the most powerful compared to AMD and Nvidia GPUs, operates at a power efficiency of only 180 watts, achieving up to 2.25 times the energy efficiency of LLM inference GPUs [8][9]. - LG's AI research department found RNGD to be an effective solution for deploying Exaone models, with specific performance targets set during testing [11][16]. Group 3: Performance and Efficiency - RNGD's performance is approximately 1.4 TeraFLOPS per watt, making it competitive in terms of efficiency, especially for inference tasks [10][17]. - The chip's memory bandwidth of 1.5TB/s is crucial for LLM inference, allowing for faster token generation [10][11]. - FuriosaAI's architecture is designed to minimize data movement and maximize efficiency, which is a significant advantage over traditional GPU architectures [9][10]. Group 4: Market Position and Future Outlook - FuriosaAI faces challenges in competing with Nvidia and AMD, which offer higher performance and efficiency, but the company is confident in its architecture's scalability [17][18]. - The demand for autonomous AI models and infrastructure is growing, positioning FuriosaAI favorably in the market despite the competition [16][18]. - The company plans to expand its architecture to compete with the latest GPU technologies, leveraging its established design and software stack [18].