芯片新贵，集体转向

Core Viewpoint - The AI chip market is shifting focus from training to inference, with companies like Graphcore, Intel, and Groq adapting their strategies to capitalize on this trend as the training market becomes increasingly dominated by Nvidia [1][6][12]. Group 1: Market Dynamics - Nvidia remains the leader in the training chip market, with its CUDA toolchain and GPU ecosystem providing a significant competitive advantage [1][4]. - Companies that previously competed in the training chip space are now pivoting towards the more accessible inference market due to high entry costs and limited survival space in training [1][6]. - The demand for AI chips is surging globally, prompting companies to seek opportunities in inference rather than direct competition with Nvidia [4][12]. Group 2: Company Strategies - Graphcore, once a strong competitor to Nvidia, is now focusing on inference, having faced challenges in the training market and experiencing significant layoffs and business restructuring [4][5][6]. - Intel's Gaudi series, initially aimed at training, is being repositioned to emphasize both training and inference, with a focus on cost-effectiveness and performance in inference tasks [9][10][12]. - Groq has shifted its strategy to provide inference-as-a-service, emphasizing low latency and high throughput for large-scale inference tasks, moving away from the training market where it faced significant barriers [13][15][16]. Group 3: Technological Adaptations - Graphcore's IPU architecture is designed for high-performance computing tasks, particularly in fields like chemistry and healthcare, showcasing its capabilities in inference applications [4][5]. - Intel's Gaudi 3 is marketed for its performance in inference scenarios, claiming a 30% higher inference throughput per dollar compared to similar GPU chips [10][12]. - Groq's LPU architecture focuses on deterministic design for low latency and high throughput, making it suitable for inference tasks, particularly in sensitive industries [13][15][16]. Group 4: Market Trends - The shift towards inference is driven by the lower complexity and resource requirements compared to training, making it more accessible for startups and smaller companies [22][23]. - The competitive landscape is evolving, with a focus on cost, deployment, and maintainability rather than just computational power, indicating a maturation of the AI chip market [23].