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Meta and Berkeley developed StreamDiT for real-time AI video generation: 1) 16fps 512p on single GPU, 4B-param model creates 1-min videos with live edits; 2) Novel buffer enables parallel processing (2 frames/0.5s) with 8-step optimization; 3) Trained on 3K HD videos + 2.6M dataset; 4) Outperforms rivals in motion smoothness, 30B-param version shows quality potential; 5) Enables interactive video despite transition flaws.....

Google released HAI-DEF toolkit with MedGemma (medical text/records) and MedSigLIP (image analysis) models. Supports local GPU deployment, privacy protection, and customization via Hugging Face/Vertex AI.....

Tencent officially launched and open-sourced a new member of the Hypermix large model family - the Hypermix-A13B model. The model adopts an expert mixture (MoE) architecture, with a total parameter scale of 80 billion and an activated parameter count of 13 billion. It maintains the performance of top-tier open-source models while significantly reducing inference latency and computational costs, providing a more cost-effective AI solution for individual developers and small and medium-sized enterprises.

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, 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, Mistral AI, headquartered in Paris, France, in partnership with All Hands AI, has launched an open-source AI model designed specifically for software development — Devstral. This model boasts up to 24 billion parameters and is currently in the "research preview" stage, released under the Apache2.0 open license, allowing developers and enterprises to use it freely in commercial applications. The release of Devstral marks a significant step forward for AI in the programming field.

Aethir, a decentralized GPU cloud computing provider, today announced the launch of an industry alliance called "AI Unbundled" to advance the Web3 artificial intelligence ecosystem. This initiative brings together leading organizations including 0G Labs, Biconomy, Polyhedra, Oasis Protocol Foundation, ChainGPT, IoTeX, iExec, Geodnet, Flock.io, and Alpha Ne.

Intel recently announced the open-sourcing of its AI Playground software, designed for local generative AI. AI Playground provides a powerful platform for running AI models on Intel Arc GPUs. It supports various image and video generation models, as well as Large Language Models (LLMs), significantly lowering the hardware barrier for AI applications by optimizing local computing resources. The project is available on GitHub and has attracted developers and AI enthusiasts worldwide.

Intel has announced the open-sourcing of its generative AI software, AI Playground, generating significant interest within the AI community. Optimized for Intel Arc GPUs and integrated graphics, AI Playground is described as an 'AI hub' that supports local running of chat-based Large Language Models (LLMs), as well as image and video generation capabilities. This open-sourcing signifies Intel's commitment to advancing the accessibility of generative AI technology.

AMD's advancements in AI are noteworthy, particularly its latest optimizations for the Stable Diffusion model. Recently, Stability AI released an ONNX-optimized version of Stable Diffusion, resulting in significantly improved performance for AMD Radeon GPUs and Ryzen integrated graphics in AI tasks, with speed increases up to 3.8 times faster. This progress narrows the gap with NVIDIA's ecosystem...

Former Intel CEO Pat Gelsinger recently criticized Nvidia's pricing strategy for its AI GPUs on the Acquired podcast during Nvidia's 2025 GPU Technology Conference, arguing that the high cost makes them unsuitable for large-scale AI inference tasks. Gelsinger highlighted that inference is crucial for deploying AI models and that the industry should focus more on inference, a domain where Nvidia's technology falls short in terms of cost-effectiveness. Image source omitted.

Meituan CEO Wang Xing detailed the company's strategic plan for artificial intelligence (AI). Wang revealed that over the past year, Meituan prioritized securing GPU resources, investing heavily in AI infrastructure. He further stated that Meituan plans to further increase investment in key AI infrastructure in 2025 to strengthen its position in this field.

HPC-AI Tech recently announced Open-Sora 2.0, a groundbreaking video AI system achieving commercial-grade quality at approximately one-tenth the typical training cost. This represents a potential paradigm shift in the resource-intensive field of video AI, mirroring efficiency gains seen in language models. While existing high-quality video generation systems like Movie Gen and Step-Video-T2V can cost millions of dollars to train, Open-Sora 2.0 offers a significantly more affordable alternative.

In the midst of the AI revolution, no company is better positioned than Nvidia. Since ChatGPT ignited the market over two years ago, the high-performance GPU giant's revenue, profitability, and cash reserves have skyrocketed, driving its stock price higher. Fueled by its growing wealth, Nvidia is aggressively expanding its investments in AI, particularly in startups. According to PitchBook data, Nvidia significantly increased its venture capital activity in 2024, participating in 49 rounds of AI-related funding.

Google CEO Sundar Pichai announced at a launch event that Google has open-sourced its latest multimodal large model, Gemma-3. This model is attracting significant attention for its low cost and high performance. Gemma-3 offers four different parameter scale options: 1 billion, 4 billion, 12 billion, and 27 billion parameters. Surprisingly, the largest 27 billion parameter model only requires a single H100 GPU for efficient inference, while similar models often require ten times the computing power.