2025年大模型推理优化与部署实践产业洞察研究报告-云计算开源产业联盟

Group 1 - The core point of the report indicates that the large model industry has transitioned from "model innovation" to a critical period of "scale implementation," where inference optimization and efficient deployment have become core competitive advantages, leading to rapid market growth [1][13] - The global AI inference computing power market is expected to grow nearly tenfold from 2021 to 2024, reaching USD 13.958 billion in 2024 and projected to increase to USD 18.355 billion in 2025; the Chinese market is expected to grow even faster, reaching CNY 43.83 billion by 2025, with a compound annual growth rate of 66.3% [1][39][43] - The market competition landscape is diverse, with Tianyi Cloud, Alibaba Cloud, and Huawei Cloud leading the domestic market, while Amazon, Google, and Microsoft dominate internationally; the token-based billing model has become mainstream, and the model-as-a-service (MaaS) business model is rapidly gaining popularity [1][39] Group 2 - The current deployment forms have diversified to meet different scenario needs, with four main deployment methods emerging: MaaS, integrated inference machines, private deployment platforms, and cloud-edge-end collaborative inference [2][59] - Full-stack optimization technology has become a core support, breaking through performance bottlenecks through hardware adaptation, inference engines, model layers, and parallel computing technologies [2][3] - The industry faces multiple challenges, including high costs, lack of standards, talent shortages, fragmented ecosystems, and complex security compliance; the report suggests accelerating the establishment of a technical standard system and fostering collaborative innovation mechanisms [3][14] Group 3 - Industry applications are deeply rooted, with significant results from practical cases; for instance, CITIC Securities has processed over 200 million service requests through an inference acceleration engine, and a robotics company has achieved an 80% efficiency improvement in private deployment [3][14] - The Chinese AI inference computing power market is expected to see a rapid increase in the proportion of inference workloads, projected to reach 70.5% by 2026, indicating a shift in focus from training to inference [43][47] - The deployment preferences for large model inference platforms are expected to change significantly, with public cloud deployment increasing from 49% to 58% and private cloud deployment rising from 16% to 26% by 2027 [58][59]