Via cnet.com
Job postings in Ulanqab reveal DeepSeek's first known data center location as the firm prepares to launch its trillion-parameter V4 model
DeepSeek has been one of the more disruptive forces in AI over the past year, releasing models that punched well above their weight class against American competitors. Now the Hangzhou-based startup is making its infrastructure ambitions official, posting job listings for data center roles in Ulanqab, a city in Inner Mongolia, on April 2, 2026.
These weren’t vague corporate postings. The roles, specifically a Senior Delivery Manager and Server Maintenance Engineers, signal that DeepSeek is moving from model-maker to full-stack AI operator with its own physical compute backbone.
Why Inner Mongolia, and why now #
Inner Mongolia is not a random pick. The region has become something of a quiet data center capital for Chinese tech, and the reasons are straightforward.
Electricity is cheap there, the climate is cool enough to reduce cooling costs significantly, and the land is abundant. For high-density AI compute operations, those three factors matter enormously.
The region already hosts established infrastructure from operators including China Telecom, meaning DeepSeek isn’t building into a vacuum. There’s existing grid capacity, network connectivity, and an ecosystem of technical workers to draw from.
The timing also aligns with something specific on DeepSeek’s product roadmap. The company is preparing to launch DeepSeek-V4, its next flagship model, with the anticipated release window around April 24, 2026. V4 is expected to deploy a Mixture of Experts architecture, which is a design approach where a model routes different inputs through specialized sub-networks rather than activating the entire model for every query.
The parameter count being discussed for V4 is up to 1.6 trillion, with 49 billion active parameters. A 1.6 trillion parameter model would represent a significant leap, and running it at inference scale requires substantial physical infrastructure.
The hardware question that won’t go away #
There’s an obvious elephant in the room here. DeepSeek operates under the shadow of US export controls that restrict China’s access to advanced Nvidia chips, particularly the high-end data center GPUs that power most frontier AI training runs in the West.
DeepSeek’s earlier models attracted attention precisely because they appeared to achieve competitive results with fewer high-end chips than expected. The company has reportedly worked with Nvidia Blackwell-variant chips in past training efforts, though the precise hardware configuration for its new data center remains undisclosed.
Building a large proprietary data center in China rather than leasing compute from international cloud providers is partly a hedge against supply chain uncertainty. If access to Western hardware tightens further, owning your own facility gives you more control over what goes inside it, including domestic Chinese alternatives like chips from Huawei’s Ascend line.
This is what makes the Inner Mongolia announcement strategically significant beyond just the real estate play. It represents DeepSeek’s first known public commitment to a physical location, which signals a level of permanence and scale that job postings alone don’t fully capture.
What this means for investors and the competitive landscape #
DeepSeek’s move is part of a broader pattern of Chinese AI firms building domestic compute capacity rather than depending on foreign cloud infrastructure. That trend accelerates as US-China tech tensions persist, and it creates a two-track AI development ecosystem globally, one centered on US hyperscalers and one increasingly self-contained within China.
The V4 launch is a near-term catalyst. A 1.6 trillion parameter model released by a firm that already rattled the AI industry with its earlier releases would likely generate significant market attention. If V4 performs competitively against top American models, the narrative around Chinese AI capabilities shifts again.
The infrastructure build-out itself has supply chain implications. Equipping a large-scale Chinese AI data center draws on domestic chip suppliers, cooling equipment manufacturers, and power infrastructure providers.
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