Kimi杨植麟称“训练成本很难量化” 仍将坚持开源策略

Core Insights - Kimi, an AI startup, has released its latest open-source model, Kimi K2 Thinking, with a reported training cost of $4.6 million, significantly lower than competitors like DeepSeek V3 at $5.6 million and OpenAI's GPT-3, which costs billions to train [2][3] - The company emphasizes ongoing model updates and improvements, focusing on absolute performance while addressing user concerns regarding inference length and performance discrepancies [2][3] - Kimi's models are gaining traction in the international market, with five Chinese open-source models listed among the top twenty on the OpenRouter platform [3][5] Company Strategy - Kimi plans to maintain its open-source strategy and prioritize the application and optimization of the Kimi K2 Thinking model, while also developing multimodal models [5] - The company aims to differentiate itself from leading competitors like OpenAI by focusing on architectural innovation, open-source strategies, and cost control, avoiding direct competition in specific AI browser markets [5] Technical Aspects - Kimi utilizes H800 GPUs with InfiniBand technology for high-performance computing and AI training, despite having fewer and less powerful chips compared to U.S. counterparts [3] - The training cost and resource allocation for Kimi K2 Thinking are primarily directed towards research and experimentation, making precise cost quantification challenging [2]