{"id":398695,"date":"2026-07-01T03:05:29","date_gmt":"2026-07-01T03:05:29","guid":{"rendered":"https:\/\/bizscoreai.com\/blog\/?p=398695"},"modified":"2026-07-01T03:05:29","modified_gmt":"2026-07-01T03:05:29","slug":"meituan-longcat-2-0-trained-on-chinese-chips","status":"publish","type":"post","link":"https:\/\/bizscoreai.com\/blog\/meituan-longcat-2-0-trained-on-chinese-chips\/","title":{"rendered":"Meituan&#8217;s LongCat-2.0 Was Trained Entirely on Chinese Chips, the Company Says"},"content":{"rendered":"<p class=\"post-meta-row\"><span class=\"post-meta-time\">6 min read<\/span> \u00b7 <span class=\"post-meta-updated\">Last updated 2026-06-30<\/span><\/p>\n<nav class=\"post-toc\" aria-label=\"Table of contents\"><strong>In this article<\/strong><\/p>\n<ol>\n<li><a href=\"#why-it-matters\">Why It Matters<\/a><\/li>\n<li><a href=\"#whats-new\">What&#8217;s New<\/a><\/li>\n<li><a href=\"#the-numbers\">The Numbers<\/a><\/li>\n<li><a href=\"#what-comes-next\">What Comes Next<\/a><\/li>\n<li><a href=\"#what-this-means\">What This Means in Practice<\/a><\/li>\n<li><a href=\"#the-bigger-picture\">The Bigger Picture<\/a><\/li>\n<\/ol>\n<\/nav>\n<p class=\"wp-block-paragraph\">The most striking thing about Meituan&#8217;s new AI model is not its size, though it is enormous, but the hardware it ran on. The Chinese delivery and services giant launched LongCat-2.0 this week and says it is the first model of its scale trained entirely on domestically developed chips, a claim aimed squarely at the US export controls that have kept the best American silicon out of Chinese hands.<\/p>\n<h2 class=\"wp-block-heading\" id=\"why-it-matters\">Why It Matters<\/h2>\n<p class=\"wp-block-paragraph\">For years the open question over China&#8217;s AI sector has been whether it can build frontier-scale models without Nvidia. Washington restricts exports of the most advanced chips on national security grounds, betting that limited access to cutting-edge silicon would slow China&#8217;s progress. A 1.6-trillion-parameter model that Meituan says was both trained and served on home-grown hardware is a direct test of that bet. If the claim holds, the single biggest lever the US has used to contain Chinese AI looks less decisive than it did.<\/p>\n<h2 class=\"wp-block-heading\" id=\"whats-new\">What&#8217;s New<\/h2>\n<p class=\"wp-block-paragraph\">LongCat-2.0 carries 1.6 trillion parameters and a context window of one million tokens, and Meituan says its performance is comparable to Google&#8217;s Gemini 3.1 Pro, released in February. The company describes it as &#8220;the industry&#8217;s first trillion-parameter model to complete end-to-end training and inference on a 50,000-chip domestic compute cluster.&#8221; The model has been open-sourced, putting the weights in the hands of anyone who wants to run or scrutinise them.<\/p>\n<p class=\"wp-block-paragraph\">The crucial phrase is &#8220;end-to-end.&#8221; Plenty of Chinese models already run inference, the comparatively light task of answering a query once a model is built, on domestic hardware. Pre-training is the heavy part, the computationally brutal process in which a model digests vast data sets to learn its basic patterns, and it is where the most advanced chips have mattered most. Meituan&#8217;s claim that LongCat-2.0 was both pre-trained and served on domestic silicon is what makes the announcement more than a marketing line.<\/p>\n<h2 class=\"wp-block-heading\" id=\"the-numbers\">The Numbers<\/h2>\n<ul class=\"wp-block-list\">\n<li><strong>1.6 trillion parameters,<\/strong> putting LongCat-2.0 among the largest models publicly announced.<\/li>\n<li><strong>1 million token context window,<\/strong> for long-document and long-session work.<\/li>\n<li><strong>50,000-chip domestic compute cluster,<\/strong> used for what Meituan calls end-to-end training and inference.<\/li>\n<li><strong>Comparable to Google Gemini 3.1 Pro,<\/strong> by Meituan&#8217;s own account, on the benchmarks it cites.<\/li>\n<li><strong>Fully open-sourced weights,<\/strong> available for anyone to run or scrutinise.<\/li>\n<\/ul>\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>&#8220;The industry&#8217;s first trillion-parameter model to complete end-to-end training and inference on a 50,000-chip domestic compute cluster.&#8221;<\/p>\n<footer>Meituan, describing LongCat-2.0<\/footer>\n<\/blockquote>\n<h2 class=\"wp-block-heading\" id=\"what-comes-next\">What Comes Next<\/h2>\n<p class=\"wp-block-paragraph\">Independent verification will come from the open-source community, which can now run LongCat-2.0 against the benchmarks Meituan cites and test whether it genuinely matches a model like Gemini 3.1 Pro. The training-hardware claim is harder for outsiders to confirm, since it rests on Meituan&#8217;s account of its own infrastructure, and that caveat is worth holding in mind alongside the company&#8217;s confidence. LongCat-2.0 is the software counterpart to a broader hardware push: China recently claimed the supercomputing crown without US chips, and domestic challengers such as Alibaba&#8217;s T-Head unit are promoting home-grown accelerators like the Zhenwu M890 GPU.<\/p>\n<figure class=\"wp-block-pullquote\">\n<blockquote class=\"pull-quote\">\n<p>Each frontier-scale model trained without American hardware narrows the gap the export controls were meant to widen.<\/p>\n<\/blockquote>\n<\/figure>\n<h2 class=\"wp-block-heading\" id=\"what-this-means\">What This Means in Practice<\/h2>\n<p class=\"wp-block-paragraph\">For anyone building on AI, the story is a reminder that the supply of capable models is globalising, not narrowing. An open-source model at this scale lowers the cost of frontier capability and widens the pool of providers beyond the familiar US names. Meituan itself is an unlikely flag-bearer, better known for food delivery than frontier AI, and its motive is concrete: routing, demand forecasting, and customer service all run on compute, and a model trained on domestic silicon insulates that compute from the next turn of the export-control screw. The practical takeaway for teams elsewhere is to keep an eye on open-weight models from outside the US, because the best price-to-performance option may increasingly come from an unexpected source.<\/p>\n<h2 class=\"wp-block-heading\" id=\"the-bigger-picture\">The Bigger Picture<\/h2>\n<p class=\"wp-block-paragraph\">At its base, the AI contest between China and the United States has become a race over chips. Export controls were designed to widen America&#8217;s lead by denying China the hardware to train the largest models. Every credible claim of a frontier-scale model trained on domestic silicon chips away at that strategy. Meituan&#8217;s announcement is one more data point in a contest Washington built its restrictions to win, and that Beijing is determined to prove it can run on its own terms.<\/p>\n<h2 id=\"faq\">Frequently Asked Questions<\/h2>\n<div class=\"post-faq\">\n<details class=\"faq-item\">\n<summary>What is LongCat-2.0?<\/summary>\n<div class=\"faq-answer\">LongCat-2.0 is Meituan&#8217;s new large language model, a 1.6-trillion-parameter system with a one-million-token context window. It has been open-sourced, and Meituan says its performance is comparable to Google&#8217;s Gemini 3.1 Pro.<\/div>\n<\/details>\n<details class=\"faq-item\">\n<summary>Why is training on domestic chips significant?<\/summary>\n<div class=\"faq-answer\">Pre-training is the most compute-intensive stage of building a model and the point where the most advanced chips have mattered most. Completing it end-to-end on domestically developed hardware suggests China can build frontier-scale models without US silicon, the exact outcome that export controls were meant to prevent.<\/div>\n<\/details>\n<details class=\"faq-item\">\n<summary>Has the claim been independently verified?<\/summary>\n<div class=\"faq-answer\">Not yet. The open-source community can test the benchmark claims now that the weights are public, but the training-hardware claim rests on Meituan&#8217;s own account of its infrastructure and is harder for outsiders to confirm directly.<\/div>\n<\/details>\n<details class=\"faq-item\">\n<summary>Who is Meituan?<\/summary>\n<div class=\"faq-answer\">Meituan is a Chinese delivery and services giant best known for food delivery. It is now one of several Chinese internet companies treating AI model development as core infrastructure rather than a side project.<\/div>\n<\/details>\n<details class=\"faq-item\">\n<summary>What does open-sourcing the model accomplish?<\/summary>\n<div class=\"faq-answer\">It seeds adoption among developers, signals confidence that the domestic chips can keep up, and lets outsiders scrutinise the weights. It is a competitive move as much as a technical one.<\/div>\n<\/details>\n<details class=\"faq-item\">\n<summary>Does this end the impact of US chip export controls?<\/summary>\n<div class=\"faq-answer\">No, but it narrows the gap. Each frontier-scale model trained without American hardware weakens the leverage those controls were designed to provide.<\/div>\n<\/details>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Meituan says its new open-source LongCat-2.0, a 1.6-trillion-parameter model, was pre-trained and served entirely on domestically developed Chinese chips, a direct challenge to US export controls on advanced silicon.<\/p>\n","protected":false},"author":1,"featured_media":398696,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"rank_math_title":"","rank_math_description":"","rank_math_focus_keyword":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-398695","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-news"],"elementor_data":null,"elementor_edit_mode":null,"_links":{"self":[{"href":"https:\/\/bizscoreai.com\/blog\/wp-json\/wp\/v2\/posts\/398695","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bizscoreai.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/bizscoreai.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/bizscoreai.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/bizscoreai.com\/blog\/wp-json\/wp\/v2\/comments?post=398695"}],"version-history":[{"count":1,"href":"https:\/\/bizscoreai.com\/blog\/wp-json\/wp\/v2\/posts\/398695\/revisions"}],"predecessor-version":[{"id":398697,"href":"https:\/\/bizscoreai.com\/blog\/wp-json\/wp\/v2\/posts\/398695\/revisions\/398697"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/bizscoreai.com\/blog\/wp-json\/wp\/v2\/media\/398696"}],"wp:attachment":[{"href":"https:\/\/bizscoreai.com\/blog\/wp-json\/wp\/v2\/media?parent=398695"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/bizscoreai.com\/blog\/wp-json\/wp\/v2\/categories?post=398695"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/bizscoreai.com\/blog\/wp-json\/wp\/v2\/tags?post=398695"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}