From 8-GPU servers to supernodes: AI data centers for the inference era
Speaking at the 2026 ITValue Summit on September 17, xFusion computing vice president Wang Liangdong said AI has entered an inference-dominated phase in which global token consumption keeps surging and buyers judge infrastructure by cost per token, throughput and cluster stability rather than peak per-card performance. The article cites a range of figures: the 2026 China Development Forum said China's daily token calls topped 140 trillion in March, OpenRouter data showed China accounted for 36% of global token calls in the March 16-22 week, JPMorgan expects Chinese inference token consumption to grow roughly 370x in five years, and IDC projects annual global consumption of 150,000 Peta Tokens by 2030. It argues the 8-GPU server paradigm hits communication bottlenecks under trillion-parameter, high-concurrency inference, pointing to Huawei's Ascend 960 supernode (4,096 cards per node, 8E FP8), Lenovo's Wanquan heterogenous computing platform V5.0 and xFusion's FusionPoD for AI rack-scale designs — while warning that supernodes only suit very large workloads, interconnect standards remain unsettled, and utilization depends on sustained paying demand.
The yardstick for AI infrastructure spending is shifting from training peak performance to cost per token and long-run utilization, which shapes how enterprises buy and size compute.
(本文作者为 硅碳变量,钛媒体经授权发布)
文|硅碳变量,作者|沈浪
9月17日,在2026 ITValue Summit数字价值年会上,超聚变算力领域副总裁王梁栋指出,伴随大模型能力加速演进,AI进入推理为主的“干活”时代,全球Token消耗将持续爆发,促使AI基础设施同步进化。
当下算力行业的竞争逻辑正在迭代,单纯依靠堆叠服务器和扩张硬件规模的粗放式发展路径,效用持续减弱。
随着AI重心从模型训练转向海量推理,算力基础设施正在经历底层变革,传统“堆硬件、扩规模”的建设思路,越来越难以匹配新阶段的产业需求。
Token的真实账单
Token不是噱头,是AI产业正在面对的真实账单。
#xFusion#Huawei#Ascend 960#Lenovo#CAICT#OpenRouter#IDC#JPMorgan