Stock Markets July 7, 2026 06:57 AM

DeepSeek Moves to Build Its Own AI Chip, Pressuring Nvidia Shares

Chinese AI startup developing inference-focused semiconductor as it boosts chip-design hiring and supplier talks

By Caleb Monroe
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A report on Tuesday said DeepSeek is developing a custom semiconductor for AI inference workloads, a project that could reduce its reliance on Nvidia and Huawei chips. Nvidia shares dipped 1.6% in premarket trading by 06:59 ET (10:59 GMT) after the report. The effort, which dates back about a year, is still at an early stage but has involved discussions with chip-design firms, foundries and memory suppliers and increased, quiet hiring of chip-design engineers. Significant obstacles remain, including time, capital and U.S. export restrictions affecting access to advanced foundries and high-bandwidth memory.

DeepSeek Moves to Build Its Own AI Chip, Pressuring Nvidia Shares
NVDA
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Key Points

  • DeepSeek is developing a semiconductor focused on AI inference workloads rather than training.
  • Nvidia shares fell 1.6% in premarket trading by 06:59 ET (10:59 GMT) after the report about DeepSeek's chip plans.
  • The project dates back about a year and has involved talks with chip-design firms, foundries and memory suppliers, alongside quiet hiring of chip-design engineers.

Summary

A recent report said DeepSeek has begun work on a purpose-built semiconductor for AI computing, targeting inference tasks rather than model training. The announcement coincided with a 1.6% drop in Nvidia shares in premarket trading by 06:59 ET (10:59 GMT).


Market reaction

Nvidia stock slid 1.6% in premarket activity by 06:59 ET (10:59 GMT) after publication of the report describing DeepSeek's chip plans. The move in Nvidia shares followed news that DeepSeek is pursuing its own silicon, a shift that could alter the startup's dependence on external chip suppliers.

Technical focus: inference, not training

The semiconductors under development at DeepSeek are intended for inference work - the stage when an already-trained AI model generates answers for users - rather than for training models from scratch. That product focus aligns the project with the fastest-growing segment of AI compute demand as workloads increasingly move from building models to executing them for end users. Inference chips can often be optimized to cost less and consume less power than general-purpose graphics processors.

Project timeline and sourcing

Although the initiative remains in its early phases, the work has been underway for roughly a year. DeepSeek has been holding talks with third-party chip-design firms, foundries and memory suppliers as part of the effort. The company has also stepped up hiring of chip-design engineers in recent months, conducting recruitment quietly rather than by posting openings on public job boards, according to the report.

Strategic implications

Developing an in-house chip would represent a meaningful strategic change for DeepSeek. The startup has been promoted within China as a national AI success story since it drew swift global attention last year with the release of two AI models noted for their efficiency - releases that rattled technology hubs abroad. Creating proprietary silicon could reduce DeepSeek's reliance on major suppliers and place it among a growing group of AI developers that have moved to design their own hardware.

Barriers and constraints

Despite the potential benefits, the report underscores that producing a competitive AI chip generally requires years and substantial capital. DeepSeek also faces constraints specific to China: U.S. export restrictions prevent Chinese chip designers from using the most advanced overseas foundries, and separate U.S. curbs have limited access to high-bandwidth memory - a component the report described as essential to inference chips.

Outlook

At present, the project is nascent and faces both technical and geopolitical hurdles. If DeepSeek continues advancing its chip program, it may increase competitive pressure on existing hardware suppliers. The company’s quiet hiring and supplier discussions suggest a deliberate, early-stage push into chip design rather than an announced, near-term commercial rollout.

Risks

  • Technical and financial risk - Building a competitive AI chip typically takes years and requires substantial capital investment, which could strain DeepSeek's resources and delay commercialization.
  • Supply and regulatory constraints - U.S. export restrictions bar Chinese designers from using the most advanced overseas foundries and separate U.S. curbs have limited access to high-bandwidth memory, both of which are important for inference chips.
  • Market and competitive pressure - If DeepSeek advances its own chip, it could intensify competition for existing hardware suppliers, including established GPU vendors.

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