Key Points:
- Chinese AI chipmakers Huawei and Cambricon raised prices on current and next-generation AI processors by 20% to 50%.
- Huawei increased quotes for its upcoming Ascend 950DT accelerator card to over 250,000 yuan ($37,255).
- A global shortage of high-bandwidth memory (HBM) controlled by SK hynix, Samsung, and Micron is driving up manufacturing costs.
- Rising hardware prices complicate Beijing’s push to replace Nvidia accelerators with domestic alternatives across cloud data centers.
Leading Chinese semiconductor manufacturers are sharply raising prices across current and next-generation artificial intelligence processors as a worsening global shortage of high-bandwidth memory (HBM) inflates manufacturing expenses. Domestic chipmakers, including Huawei Technologies, Cambricon Technologies, MetaX, and Iluvatar CoreX, have increased quotes for their flagship AI accelerators by 20% to 50%. The price hikes introduce significant cost headwinds for Chinese cloud providers and software developers working to replace American semiconductors with homegrown alternatives.
Telecommunications and technology giant Huawei instituted some of the steepest price increases across its Ascend computing portfolio. Industry procurement data shows that Huawei raised the indicated price for its upcoming Ascend 950DT accelerator card to more than 250,000 yuan ($37,255), marking an increase of 20% to 50% compared to customer quotations issued two months ago. Price pressures also hit older hardware lines, with the Ascend 950PR rising from 60,000 yuan earlier this year to more than 80,000 yuan, while the legacy Ascend 910C processor now commands over 110,000 yuan per board.
Rival domestic chip designer Cambricon Technologies made parallel pricing adjustments, lifting indicated prices for its upcoming Siyuan 690 AI processor by 20% to 30%. Smaller regional GPU designers MetaX and Iluvatar CoreX enacted similar price hikes across their enterprise accelerator lineups to protect operating margins. The price increases reflect soaring raw material and component expenses as memory fabricators allocate global cleanroom capacity to high-margin hyperscale orders.
At the center of the pricing crisis is an acute shortage of high-bandwidth memory, an essential component that determines how quickly artificial intelligence accelerators can process complex neural networks. HBM stacks dynamic random-access memory (DRAM) dies vertically on top of an interposer substrate, creating ultra-wide data buses that allow processors to exchange data at multiple terabytes per second. Without high-grade HBM modules attached directly to processor dies, artificial intelligence accelerators cannot train large multimodal models or run real-time inference without crippling latency.
Three global memory manufacturers—South Korea’s SK hynix and Samsung Electronics, alongside United States-based Micron Technology—control over 90% of the world’s high-bandwidth memory output. Surging artificial intelligence data center construction worldwide has consumed virtually all available HBM manufacturing capacity through 2027. This industry-wide supply squeeze has driven spot prices for advanced memory stacks up by 200% to 400%, forcing global and domestic hardware assemblers to bid aggressively for limited production allocations.
For Chinese chip designers, United States export controls and trade restrictions compound the global memory crunch. Washington has restricted the export of cutting-edge HBM architectures and advanced extreme ultraviolet lithography tools to mainland China. As a result, Chinese semiconductor firms must pay steep gray-market premiums for compliant memory modules or rely on domestic memory fabricators like CXMT, whose advanced HBM packaging yields remain in early commercial scaling phases.
The rising hardware prices create major commercial complications for Beijing’s strategic self-reliance initiative. Chinese state ministries have actively directed state-owned enterprises, telecommunications carriers, and internet giants to transition away from Nvidia hardware in favor of certified domestic AI chips from Huawei and Cambricon. However, as domestic accelerator prices climb toward parity with imported hardware, Chinese AI developers—including ByteDance, Tencent, Alibaba, and DeepSeek—face higher capital expenditures to build large-scale model training clusters.
In equity markets, the price hikes triggered mixed reactions across regional stock exchanges. Shares of Cambricon Technologies dipped in Shanghai as investors weighed rising component costs against expanding sales volume, while Hong Kong-listed AI hardware startups experienced modest price corrections. Financial analysts noted that while domestic chipmakers enjoy guaranteed demand from government procurement lists, rising memory expenses could compress gross profit margins if enterprise buyers delay secondary hardware orders.
Domestic packaging foundries and semiconductor fabricators are working to develop proprietary packaging architectures to reduce reliance on standard foreign HBM layouts. Chinese research institutes are experimenting with advanced 2.5D interposers, optical chiplet interconnects, and high-density standard DRAM pooling to deliver high memory bandwidth without using expensive stacked HBM dies. However, commercializing alternative packaging methods at high manufacturing yields will require several years of continuous cleanroom refinement.
As global demand for generative artificial intelligence infrastructure shows no signs of slowing down, the high-bandwidth memory bottleneck will continue to dictate semiconductor pricing dynamics worldwide. Chinese AI chipmakers face the delicate challenge of absorbing soaring component expenses while scaling domestic accelerator production. The memory shortage proves that achieving true semiconductor independence requires mastering the entire technological ecosystem, from custom compute silicon to advanced memory packaging.
