[SMM Computing Power Midday Review] H800 highest price in Beijing-Tianjin-Hebei rises, and scattered resource subletting intensifies supply tightness.

[Market Overview] This morning, the highest price in the H800 Beijing-Tianjin-Hebei region saw a notable rise. The SMM-H800-80G-Complete Unit-Beijing-Tianjin-Hebei-Monthly Subscription highest monthly rent increased from 73,000 yuan to 75,500 yuan, equivalent to a highest card-hour rate of 13.11 yuan. The uplift in high-end prices reflected tight spot supply of NVIDIA high-end computing power in the region, while demand remained elevated.

[H800 Beijing-Tianjin-Hebei: Highest Price Rose, Price-Ceiling Anchor Lifted] The SMM-H800-80G-Complete Unit-Beijing-Tianjin-Hebei-Monthly Subscription highest monthly rent increased from 73,000 yuan to 75,500 yuan, up 2,500 yuan, a gain of 3.42%; the highest card-hour rate rose from 12.67 yuan to 13.11 yuan, up 0.43 yuan, a gain of 3.39%. The core driver of this rise was: undersupply of NVIDIA spot supply in the market. A large volume of scattered computing-power resources was being leased in advance by major platforms and operators, then, after unified scheduling, subleased to earn the spread; markups in the circulation process directly pushed up the listed highest price. The rise in the highest price rather than the lowest price indicates that the shortage was concentrated in compliant spot supply that could be delivered immediately, with bargaining power tilting toward schedulers holding the resources.

[SMM View] Overall demand for computing power stayed high, and the structural imbalance on the supply side was difficult to ease in the short term. Two points warrant attention: first, scattered resources, after being “master-leased—subleased” by platforms and operators, flowed out with added markups. While this improved scheduling efficiency, it also amplified end-user prices; insufficient NVIDIA spot supply was the fundamental constraint.

Second, domestic computing power was entering the market in succession, but developers’ and consumers’ confidence in its large-model training efficiency and ecosystem compatibility still needed time to build, making it difficult in the short term to form a substantive substitute for NVIDIA high-end prices. The market called for a more transparent spot supply and pricing mechanism to compress unnecessary intermediary markups.