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Recently, Professor Zhai Jidong's team from the Institute of High Performance Computing at Tsinghua University, in collaboration with Tsinghua-affiliated innovative enterprise, Qingcheng Extreme Intelligence, announced the open-sourcing of Chitu, a groundbreaking large model inference engine. This innovative technology marks another significant breakthrough for China in the AI field, particularly in inference engine development. A core highlight of the Chitu engine is its ability to natively run FP8 precision models on non-Nvidia Hopper architecture GPUs and various domestic chips.

The ByteDance Doubao large model team announced today the successful development of a new sparse model architecture called UltraMem. This architecture effectively addresses the high memory access issues during the inference of MoE (Mixture of Experts) models, improving inference speed by 2 to 6 times compared to MoE, and reducing inference costs by up to 83%. This groundbreaking advancement opens a new path for efficient inference of large models. The UltraMem architecture successfully resolves the memory bottleneck during inference of MoE architectures while maintaining model performance. Experimental results show that the parameters and activation conditions are the same.

In the world of artificial intelligence, the reasoning capabilities of machine learning models, particularly large language models (LLMs), have been a focal point for scientists. Recently, Apple's AI research team published a paper titled 'Understanding the Limitations of Large Language Models in Mathematical Reasoning,' shedding light on these models' limitations when addressing logical problems. In the paper, researchers demonstrate this through a simple mathematical question. They first posed a question about Oliver picking kiwis: as follows: Oliver picked 44 on Friday.

The inference cost of Baidu Wenxin large model has been reduced to 1% of the original. The daily call volume of the Wenxin large model exceeds 50 million, with a quarterly growth rate of 190%. Approximately 26,000 enterprises are using the Wenxin large model, with a quarterly growth rate of 150%.

Trends in the Application of Large Models: The article mentions how large model AI is rapidly infiltrating various industries, but at the same time exposes the logical reasoning flaws of large models. Limitations of Knowledge in Large Models: A founding member of OpenAI candidly stated that the knowledge of large models is much more incomplete than you might imagine. Tests in both fictional and real scenarios indicate that large models suffer from a 'Reversal Curse' and are unable to perform reverse reasoning. Doubts About the Reliability of Large Models: Logical reasoning forms the basis for large models to solve problems correctly. The Reversal Curse indicates deficiencies in the reasoning capabilities of large models, which may impact various important application areas.