未来智造局｜“百万token一分钱” 推理GPU驱动大模型下半场发展

Core Insights - The AI industry is transitioning from a "training-driven" phase to a "reasoning-driven" phase, with reasoning computing power becoming the core element for the commercialization of AI [1][2] - Sunrise, a domestic AI chip company, has launched its new generation reasoning GPU chip, the Qihang S3, aiming for a target of "one cent per million tokens" [1][5] - The next decade will see reasoning infrastructure as the foundational base for China's AI era, emphasizing the need for cost-effective and scalable reasoning capabilities [1][9] Group 1: Reasoning Computing Power - Reasoning computing power is essential for the practical application of AI, with predictions indicating that by 2026, reasoning computing will account for 66% of AI computing, surpassing training computing for the first time [2][4] - The shift towards reasoning-driven AI is crucial for enhancing the efficiency of AI services in the real economy [2][3] Group 2: Sunrise's Innovations - Sunrise is the first company in China to focus on reasoning GPUs, having developed its first chip, Qihang S1, in 2018, and has since released the Qihang S2 and Qihang S3, which are optimized for large model reasoning scenarios [3][5] - The Qihang S3 chip aims to achieve over ten times improvement in reasoning cost-effectiveness, with current costs at approximately 0.57 yuan per million tokens, better than the market average [5][6] Group 3: Industry Challenges and Solutions - The industry faces challenges such as low resource utilization, insufficient adaptation efficiency, and complex operations, with over 40% GPU idle rates under traditional architectures [6][8] - Sunrise is collaborating with partners to create a reasoning system-level solution that optimizes both hardware and software to address these challenges and improve computing efficiency [6][8] Group 4: Market Potential and Future Trends - The demand for reasoning tokens is expected to grow exponentially, with a significant market opportunity for specialized reasoning GPUs [6][9] - The reduction of reasoning costs is projected to lead to a massive increase in AI applications, with estimates suggesting that a 50% cost reduction could trigger widespread adoption [8][9]