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Qafind Labs recently released its newly developed ChatDLM model, an innovative achievement that has drawn widespread attention in the field of artificial intelligence. ChatDLM is the first model to deeply integrate "Block Diffusion" and "Mixture of Experts (MoE)". It achieves an astonishing inference speed of 2,800 tokens/s on GPU, supporting an ultra-large context window of 131,072 tokens, ushering in a new era of document-level generation and real-time conversation.

San Francisco-based Virtue AI today announced $30 million in seed and Series A funding. The round was led by Lightspeed Venture Partners and Walden Catalyst Ventures, with participation from several other investors, including Prosperity7. Virtue AI is dedicated to eliminating the traditional trade-off between AI innovation and security, helping enterprises deploy AI safely and efficiently.

During the Qingming Festival holiday, Guangxi province implemented an AI-powered traffic model on its highways to manage and alleviate traffic congestion.

Faster data transfer speeds are crucial for advancements in AI. Lightmatter, a Mountain View-based startup valued at $4.4 billion, has announced two new technologies to accelerate connections between AI chips. Having raised $850 million in venture capital, Lightmatter is part of the burgeoning silicon photonics trend in Silicon Valley. Their innovation lies in using optical fiber to transmit data via light signals instead of traditional electrical signals.

Diffusion models have garnered significant attention in the field of AI video generation due to their superior performance. However, their inherent iterative denoising nature leads to a time-consuming and computationally expensive generation process, significantly hindering their widespread adoption. Recently, a research team from Beihang University, the University of Hong Kong, and the Shanghai AI Laboratory jointly released AccVideo, an innovative technology. This method, employing a novel and efficient distillation approach combined with a synthetic dataset, successfully boosts the generation speed of video diffusion models by an impressive 8.5 times.

Chinese AI startup DeepSeek quietly released its large language model, DeepSeek-V3-0324, sending ripples through the AI industry. The model, weighing in at 641GB, appeared on Hugging Face, showcasing DeepSeek's understated yet impactful style—a release with only an empty README file and model weights. Licensed under MIT, the model is free for commercial use and can run on consumer-grade hardware, such as a Mac equipped with an M...

Nvidia has introduced Dynamo, a new software designed to significantly accelerate the performance of its DeepSeek AI technology, promising a 30-fold speed increase.

Heard of OpenAI's extravagant Sora? With training costs easily exceeding millions of dollars, it's like the Rolls-Royce of video generation. Now, LuChern Technology announces the open-sourcing of its video generation model, Open-Sora 2.0! For a mere $200,000 (equivalent to 224 GPUs), they successfully trained a commercial-grade video generation model with 11 billion parameters. Performance rivals OpenAI Sora. Don't let the low cost of Open-Sora 2.0 fool you...

Frustrated by the slow processing speed of large language models on long documents? Researchers from Tsinghua University have unveiled a groundbreaking technology – the APB parallel inference framework – that dramatically accelerates processing. Benchmark tests show this technology achieves a 10x speed improvement over Flash Attention when handling ultra-long texts. With the rise of models like ChatGPT, AI's ability to process vast amounts of text (hundreds of thousands of words) has increased significantly. However, this often comes at the cost of processing speed...

Frustrated by the slow image generation speeds of your meticulously trained models? Luma AI recently open-sourced Inductive Moment Matching (IMM), a pre-training technique promising dramatically faster high-quality image generation. This is a significant breakthrough, accelerating the process considerably. Algorithm stagnation? Luma AI shatters the ceiling! In recent years, the AI community has...

Cerebras Systems recently announced the construction of six new data centers across North America and Europe to bolster its artificial intelligence (AI) inference capabilities. This expansion significantly increases the company's computing power, supporting the growth of various AI applications. The plan allocates 85% of computing capacity to the US, with three facilities already operational in Santa Clara and Stockton, California, and Dallas, Texas. Upcoming centers will be located in Minneapolis (expected Q2 2025), Oklahoma City, and Montreal.