Meituan trains LongCat-2.0, a 1.6T model, entirely on Chinese chips

Meituan says LongCat-2.0 is the first trillion-parameter model to complete both full training and inference on a 50,000-card domestic Chinese compute cluster ^[1]. That phrase - full training - is the whole story. Until now, Chinese labs could run finished models on home-grown silicon, but the punishing pre-training run, the part that needs tens of thousands of chips talking to each other for weeks, was assumed to require Nvidia hardware ^[2]. To coordinate that many accelerators without Nvidia's NCCL networking layer, Meituan swapped in Huawei's HCCL communication library ^[3], and community teardowns peg the hardware as Huawei Ascend-class ASIC superpods.

The architecture is what makes the scale affordable. LongCat-2.0 is a sparse mixture-of-experts design: 1.6 trillion total parameters, but only about 48 billion fire on any given token, paired with a new 'LongCat Sparse Attention' that keeps the 1-million-token context window from exploding in cost ^[4]. The point is not that the chips beat Nvidia's - they do not - it is that 'good enough' domestic silicon, wired together cleverly, was enough to train a frontier-scale model start to finish. That is the exact assumption the entire export-control strategy was built to prevent.

The most disarming detail is that LongCat-2.0 had already been judged - anonymously - and won. For roughly two months it ran on OpenRouter as a stealth model called 'Owl Alpha,' with no company name attached, and climbed to the top of global usage while processing trillions of tokens a month ^[5]. Developers were routing real production traffic to it inside coding tools before anyone knew a food-delivery company had built it.

That sequence matters because it inverts the usual order of AI hype. When Meituan finally pulled the mask off on June 30 ^[6], the reveal doubled as a credibility test the model had already passed: its ranking came from developers actually choosing it, not from a launch-day benchmark sheet, and it predated the geopolitics that now frame every headline. The community reaction reflected that - practitioners who had quietly liked 'Owl Alpha' were mostly surprised by the maker, not the quality, and the roleplay and local-model crowds treated the domestic-chip training run as the genuinely novel part.

The obvious question - why is China's DoorDash training frontier models at all - has a boring answer and an interesting one. The boring answer: Meituan needs a reasoning engine for the in-app agents that recommend restaurants and book hotels, so building rather than renting keeps that capability in-house ^[7]. Vertical integration, not moonshot ambition.

The interesting answer, argued across the coverage and echoed in developer discussion, is data. Meituan has spent years watching trillions of real-world orders move through cities - food, couriers, shops, logistics - the kind of physical-world signal no lab training purely on scraped web text possesses. Chinese tech conglomerates also sprawl across sectors in a way that makes 'a delivery company with a 1.6T model' far less strange than it sounds from the outside. Either way, the release lands as a marker that in China, frontier model-building is no longer confined to dedicated AI labs; the capability has diffused into the broader tech economy.

Meituan's own numbers are striking - a 59.5 on SWE-bench Pro that edges GPT-5.5's 58.6, plus 70-plus scores across agentic-coding suites ^[4]- but they are self-reported, and the skeptics have a real case. Independent hands-on testing has been mixed: reviewers running LongCat-2.0 through agentic coding harnesses found it capable but not a daily driver over rivals, and one-shot chat tests landed well below the headline benchmark scores. A plausible read is that it is tuned to shine as an iterative agent rather than a single-shot responder, but that is a hypothesis, not a verified result.

Access is the other catch. The API and coding plan are gated behind Chinese payment apps like Alipay and WeChat Pay, and full open weights were still listed as 'coming soon' on Hugging Face at launch ^[8]. Analysts also caution the export-control story is more nuanced than 'the wall fell' - restrictions still raise cost, slow access, and force harder engineering trade-offs even when they do not stop a model from shipping ^[9]. The honest read: a real milestone in Chinese chip independence, wrapped in benchmark claims that still deserve third-party verification.