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Nvidia Develops Groq AI Chips for Chinese Market Amid Export Shift

Nvidia Develops Groq AI Chips for Chinese Market Amid Export Shift. Source: NVIDIA Taiwan/Wikimedia Commons(CC BY 4.0)

Nvidia is reportedly developing a version of its Groq-based artificial intelligence chips tailored for the Chinese market, according to two sources familiar with the matter who spoke to Reuters. The move signals a significant strategic push by the chipmaking giant to expand its footprint in one of the world's largest technology markets.

The development follows Nvidia's licensing agreement with AI chip startup Groq, finalized late last year in a landmark $17 billion deal. At its annual developer conference held this week in San Jose, California, Nvidia unveiled a new lineup of products built around Groq's chip architecture, primarily targeting the inference segment of the AI market — where systems respond to queries, generate code, and execute user-driven tasks.

Nvidia CEO Jensen Huang confirmed during his keynote address that the company has resumed production of its H200 chips — the predecessor to its current flagship — after securing export licenses from the Trump administration and locking in purchase orders from Chinese buyers. "The inference inflection has arrived," Huang stated, projecting the revenue potential for Nvidia's AI chips could surpass $1 trillion by 2027.

One source clarified that the chips being prepared for China are not downgraded or market-specific versions. Instead, they are adaptable variants designed to integrate with a broader range of systems, with availability expected as early as May. These chips will be paired alongside Nvidia's upcoming Vera Rubin processors in its domestic lineup — hardware that currently remains restricted from Chinese export.

The inference chip market presents growing competition for Nvidia, with major Chinese technology companies like Baidu already producing proprietary inference solutions. By leveraging Groq's technology, Nvidia aims to establish a stronger competitive position in a space where its dominance is less entrenched compared to AI model training. Nvidia has not yet issued an official comment on the matter.

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