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Recently, tech giant NVIDIA, in collaboration with the Massachusetts Institute of Technology (MIT) and the University of Hong Kong, released a new framework called Fast-dLLM. This innovative framework aims to significantly increase the inference speed of diffusion models (Diffusion-based LLMs), up to 27.6 times, providing stronger technical support for AI applications. The challenges and opportunities of diffusion models make them strong competitors to traditional autoregressive models.

MegaT AI Search has launched a brand new Ultra-fast model, marking a significant breakthrough in its artificial intelligence search technology. Through innovative technical means, the response speed of MegaT AI Search has reached an amazing 400 tokens/second, ensuring that most questions can be answered within 2 seconds. This advancement not only enhances user experience but also significantly improves information retrieval efficiency. The realization of the Ultra-fast model is thanks to the application of multiple advanced technologies. The MegaT AI team has optimized on GPUs using ker

Recently, the Windows version of Sogou Input Method has undergone a major upgrade with the launch of the new AI Wanzai Quick Search function, providing users with a more efficient and convenient office and study experience. The AI Wanzai Quick Search function is characterized by its one-click trigger and fast response. Users no longer need to follow the traditional three-step search process of opening a browser👉🏻search engine👉🏻input content. They can directly type on any spot on the desktop or use the shortcut key Ctrl+Shift+Space”, which can summon the All-Purpose Quick Search” function within 0.5 seconds, allowing for faster information retrieval.

Recently, MiTA AI Search officially launched the new ultra-fast model, bringing users a more efficient and accurate search experience. The MiTA AI Search team successfully achieved a maximum response speed of 400 tokens/second on a single H800 GPU through kernel fusion technology on GPUs and dynamic compilation optimization strategies on CPUs, with most responses provided within 2 seconds.

Recently, an innovative technology named LHM (Large-scale Human Model for Animation) has achieved a significant breakthrough in the field of 3D human body reconstruction, bringing new development directions and application prospects to the field. Reconstructing animatable 3D human bodies from a single image has been a highly challenging task, plagued by ambiguities in geometry, appearance, and deformation separation. Current state-of-the-art research mostly focuses on static human modeling, and these methods often rely on synthetic 3D scans for training, which greatly limits their applications in...

On March 7th, QQ Browser announced the launch of its AI Q&A feature—Yuanbao Quick Answer—designed to provide users with faster and more concise search results. This feature leverages Tencent's HunYuan 'Fast Thinking' model Turbo S, incorporating search enhancement technology to quickly retrieve real-time information from across the web, including high-quality content from official accounts, and automatically extracts key information to deliver clear and concise answers to users.

A revolutionary technology is quietly emerging in the field of artificial intelligence. Inception Labs recently announced the Mercury series of diffusion large language models (dLLMs), a new generation of language models designed for fast and efficient high-quality text generation. Compared to traditional autoregressive large language models, Mercury boasts up to 10x faster generation speeds, achieving over 1000 tokens per second on an NVIDIA H100 GPU. This speed is...

On February 27, 2025, Tencent officially released HunYuan Turbo S, a next-generation fast-thinking model, marking a significant breakthrough in response speed and performance optimization for large language models. Unlike traditional slow-thinking models such as Deepseek R1 and HunYuan T1, HunYuan Turbo S achieves near-instantaneous responses, significantly improving the speed of answer generation.