LongCat-2.0: China's Trillion-Param Model Trained on 50K Homegrown Chips Rivals Gemini

In the escalating AI race, the question of hardware dependency has been a choke point for Chinese innovation. That changed today with Meituan's LongCat-2.0, a trillion-parameter large language model that not only rivals Google's Gemini 3.1 pro but was trained entirely on 50,000 homegrown chips. For AI researchers and engineers, this proves that domestic silicon can handle the most demanding training workloads, opening the door to a wave of advanced models that are insulated from geopolitical supply shocks.

On June 30, 2026, Chinese tech giant Meituan unveiled LongCat-2.0, a trillion-parameter large language model that it claims is the first of its scale to be trained and inferenced entirely on a 50,000-chip cluster made of domestically developed silicon. The announcement represents a watershed moment in China's push for semiconductor self-sufficiency and marks a direct challenge to the US-led export controls designed to limit Beijing's access to cutting-edge AI hardware.

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That changed today with Meituan's LongCat-2.0, a trillion-parameter large language model that not only rivals Google's Gemini 3.1 pro but was trained entirely on 50,000 homegrown chips.

The achievement comes against a backdrop of intensifying technological rivalry. Since 2023, Washington has reinforced restrictions on exports of advanced Nvidia GPUs—the workhorse of AI training globally—to China, citing national security concerns. In response, China has poured resources into domestic chip development, with companies like Huawei making strides in inference chips, but training trillion-parameter models was widely believed to require Western hardware. Meituan's disclosure that it began exploring domestic chips in 2023 underscores a multi-year strategic pivot that has now yielded a competitive model.

LongCat-2.0's performance is reportedly comparable to Google's Gemini 3.1 pro, released in February 2026. While Meituan did not name the chipmaker behind its 50,000-chip cluster, the feat demonstrates that Chinese fabricators have advanced to a level capable of supporting the most computationally demanding AI workloads. This move could accelerate the decoupling of China's AI ecosystem from US supply chains, reducing both dependency and vulnerability to sanctions.

For the US and its allies, the milestone signals that export controls may be losing efficacy as China's indigenous capabilities mature. For Nvidia, the dominant supplier of AI accelerators, it raises the specter of a permanently shrinking market in China—a region that historically contributed a significant share of its data-center revenue. The company may face increased pressure to navigate compliance while retaining Chinese customers who now have a proven alternative.

Within China, the demonstration of domestic training capacity could catalyze a new wave of AI model development. Other labs, such as DeepSeek and Zhipu, have used Chinese chips for inference but not for full-scale training. Meituan's success may encourage them to follow suit, potentially leading to a cluster of homegrown models that rival Western counterparts. It also boosts the prospects of Chinese cloud providers and AI-as-a-service platforms that can now offer sovereign AI infrastructure free of foreign hardware dependencies.

The lack of disclosure on the chipmaker introduces some uncertainty—the specific chips may be from a well-known player like Huawei or a rising startup—but the sheer scale of the deployment suggests a mature manufacturing ecosystem. The 50,000-chip cluster points to a massive investment in computing power, roughly equivalent to some of the largest AI supercomputers elsewhere, and hints at a broader buildup of China's domestic compute capacity.