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Meituan open-sourced its trillion-parameter large-scale model LongCat-2.0, and simultaneously released the inference code for domestically developed Chinese card processors.
According to Beating's monitoring, Meituan has officially open-sourced its trillion-parameter large-scale model, LongCat-2.0, with a total of 1.6T parameters and an average activation of approximately 48B, designed specifically for real-world agentic coding tasks. Architecturally, it innovatively introduces LongCat sparse attention and N-gram embedding. The former reduces fragmented memory access through flow-aware indexing and hierarchical indexing, accelerating training and inference with millions of contexts; the latter, while achieving nearly 97% sparsity in MoE, invests 135B parameters into the embedding layer, balancing parameter gains and structural stability. Post-training employs multi-teacher online distillation, categorizing experts into Agent, Inference, and Interaction types, seamlessly integrating them on a domestic computing power cluster through the MOPD architecture. As the industry's first trillion-parameter model to complete inference on a 50,000-card domestic computing power cluster, LongCat-2.0 validates the mature capability of domestic chips to handle complex large-scale model tasks. To address the multiple limitations of domestically produced Chinese chips in terms of memory, bandwidth, and interconnects, Meituan has made breakthroughs in three areas: model, chip adaptation, and deployment. At the model level, ScMoE leverages the core control capabilities of domestically produced chips to achieve physical core-level parallelism for Dense and MoE branches, combined with KV-cache partitioning to alleviate the pressure on ultra-long context memory. At the chip adaptation level, Super Kernel reduces operator startup overhead, and Weight Prefetch hides I/O latency, maximizing hardware utilization under constrained conditions. At the deployment level, PD separation is adopted to balance TTFT and TPOT, along with asynchronous Expert-Parallel load balancing to solve load unevenness under high EP (efficiency level). This open-source release simultaneously provides multiple precision versions, including BF16, FP8, and INT8, and fully opens up inference results optimized for domestic computing power, aiming to enable existing domestically produced cards and even older cards to smoothly deploy trillion-model inference services. --------------------------------- Click the original link below to join the Beating · Lark AI news channel and monitor global AI hot topics and news 24/7.
MiniMax plans to launch a large-scale model with 2.7 trillion parameters.
Mars Finance reported on July 8th that Rare Earth Technology plans to launch a new generation of large-scale models with 2.7 trillion parameters. (Science and Technology Innovation Board Daily)
Ministry of Human Resources and Social Security: By 2030, the large-scale model of the human resources and social security industry will be mature and complete, and the artificial intelligence application system will be basically sound.
According to Mars Finance, the Ministry of Human Resources and Social Security, the National Development and Reform Commission, the Ministry of Industry and Information Technology, and the National Data Administration recently jointly issued the "Implementation Opinions on Accelerating the Application and Development of 'Artificial Intelligence + Human Resources and Social Security'". A relevant official from the Ministry of Human Resources and Social Security answered reporters' questions regarding the "Implementation Opinions". In terms of promoting the work, a three-step work goal will be achieved within five years. First, building the foundation. Taking this year (2026) as a benchmark, the initial formation of the artificial intelligence application system, standard system, and guarantee system in the human resources and social security sector will be promoted. The infrastructure for "Artificial Intelligence + Human Resources and Social Security" applications will be deployed, a number of high-performance human resources and social security industry large-scale models and intelligent agent applications will be cultivated, and about 20 application scenarios based on human resources and social security industry large-scale models and corresponding high-quality datasets will be created, forming a collaborative development ecosystem of computing power, models, data, and scenario applications. Second, popularization and promotion. By 2027, a number of human resources and social security industry large-scale models and intelligent agents will be widely applied, and about 50 high-value application scenario empowerment paths will be explored, achieving significant results in intelligent development. Third, widespread application. By 2030, high-quality datasets will be effectively supplied, large-scale models for the human resources and social security industry will be mature and complete, and the artificial intelligence application system will be basically sound, forming an innovative landscape where artificial intelligence is widely applied in the human resources and social security sector. (Cailian Press)
Some large-scale AI models in China are 90% cheaper than those in the US; Chinese AI's high cost-effectiveness is capturing the US market.
According to a report by CNBC on July 7th, influenced by the continued price increases of models from leading US AI vendors, Chinese AI large-scale models are rapidly expanding their application scale in US enterprises due to their cost-effectiveness advantage. Industry insiders point out that the performance of some leading open-source and open weighted models in China is currently about 6 to 9 months behind the technology of top-tier US models such as OpenAI and Anthropic, while the price is 60% to 90% lower, and they can cover the vast majority of routine AI tasks, thus gaining popularity among US enterprises. According to statistics from the AI model aggregation platform OpenRouter, since February 8th of this year, the proportion of Chinese AI models used by US enterprises has exceeded 30% weekly, reaching a peak of 46%; while the average proportion in the previous 12 months was 11%. Another industry statistic shows that in the first week of the launch of Zhipu's latest large-scale model GLM 5.2, the daily average number of word calls increased by 27 times and the number of customers increased by 80 times, making it the fastest-deployed model on the platform in 2026; US AI startup Lindy has significantly reduced costs after switching all its AI business to DeepSeek models, and expects to save millions of dollars within a few months. (CCTV Finance)
Large-scale model stocks in Hong Kong extended their gains, with MINIMAX and Zhipu rising over 16%.
Mars Finance reported on July 8th that MINIMAX-W (00100.HK) rose 17%, and Zhipu (02513.HK) rose 16%. In terms of news, Zhipu, a leading Hong Kong-listed large-scale model manufacturer, saw its first share lock-up period expire today, with several core institutional investors clearly choosing to continue their investment. (Science and Technology Treasure Broadcast)
Analysis: Strategy sold off its first large-scale BTC transaction in five years, but the market did not show excessive panic.
According to BlockBeats, on July 7th, Crypto Quant analyst Axel Adler Jr. reported that Strategy (formerly MicroStrategy) recently sold 3,588 BTC, worth approximately $216 million, marking the company's largest Bitcoin sale in history. However, the market did not experience a significant drop, with the BTC price remaining around $63,000. This is Strategy's first large-scale net sale since December 2022. The sale was completed in two batches: 1,363 BTC were sold between June 29th and 30th at an average price of approximately $59,256, generating $80.8 million; 2,225 BTC were sold between July 1st and 5th at an average price of approximately $60,773, generating $135.2 million, for a total of approximately $216 million. This sale is primarily intended to pay preferred stock obligations and replenish dollar reserves, and does not represent a change in Strategy's long-term Bitcoin strategy. The company currently holds approximately 843,775 BTC and approximately $2.55 billion in dollar reserves. This sale represents only about 0.4% of its holdings, indicating more liquidity management than a signal of divestment. From the derivatives market perspective, the news of Strategy's sale led to a significant cooling of sentiment in the Bitcoin futures market. The composite market index fell from the bullish zone of around 80 on July 6th to 32.6, entering the bearish zone, and at one point approached 20, indicating that leveraged funds began to shift towards a defensive stance. However, the Bitcoin price reacted only moderately, currently remaining above its 30-day fair value. The market tends to view this sale as a passive liquidity operation rather than a systematic exit from Bitcoin by Strategy. The market is currently in a "neutral to cautious" state, with relatively stable price performance, but derivatives positions have clearly weakened. If the overall market index rises back above 55, it may indicate a recovery in market risk appetite; if it remains below 45 for an extended period, it could further drag BTC down below its fair value.