Core Insights - The cost of AI token generation can be reduced from over 10 yuan per million tokens to just 1 yuan through the use of Inspur's HC1000 AI server [2] - The response speed of AI systems is critical for their commercial viability, with a target of reducing latency from 15ms to 8.9ms [2][5] - The commercialization of AI agents hinges on three key factors: capability, speed, and cost, with speed being the most crucial for real-world applications [3][5] Cost and Speed - The average token generation speed for global API service providers is around 10-20 milliseconds, while domestic speeds exceed 30 milliseconds, necessitating innovations in underlying computing architecture [4] - In financial scenarios, response times must be under 10ms to avoid potential asset losses, highlighting the importance of speed in high-stakes environments [5] - The cost of tokens is a significant barrier for many enterprises, with the average cost per deployed AI agent ranging from $1,000 to $5,000, and token consumption expected to grow exponentially in the next five years [7][8] Technological Innovations - The DeepSeek R1 model achieves a token generation speed of just 8.9 milliseconds on the SD200 server, marking it as the fastest in the domestic market [5] - The architecture of AI systems must evolve to support high concurrency and large-scale applications, with a focus on decoupling computational tasks to enhance efficiency [9][10] - The HC1000 server employs a "decoupling and adaptation" strategy to significantly reduce inference costs, achieving a 1.75 times improvement in performance compared to traditional systems [10]