LLaMA Fine-tuning Reduces GPU Memory Requirements by Half, Tsinghua Proposes 4-bit Optimizer

Wikipedia Releases AI Training Dataset to Curb Web Scraping

Wikipedia recently announced the release of a dataset optimized for AI model training, in collaboration with Kaggle, Google's data science community platform. This initiative aims to reduce the scraping of Wikipedia data by AI developers, conserving the platform's bandwidth and server resources. The dataset includes structured Wikipedia information in English and French, offering high machine readability for AI developers to facilitate modeling, fine-tuning, and data analysis. The Wikimedia Foundation stated that this data...

Meta's Plan to Use EU User Data for AI Training Raises Privacy Concerns

Meta Platforms, Inc. has announced plans to use user data from its European Union applications, including Facebook and Instagram, to train its artificial intelligence models. The company clarified that the training data will include users' public posts, comments, and interactions with Meta AI, but will exclude private messages with friends and family. Training will be limited to users aged 18 and over. Meta stated it will inform its EU users of this plan this week via in-app notifications and emails.

DeepSeek and Tsinghua University Collaborate on Self-Optimizing AI Model

Amidst the growing prevalence of artificial intelligence, the collaboration between DeepSeek and Tsinghua University has garnered significant industry attention. DeepSeek, a Chinese startup, is renowned for its breakthroughs in low-cost inference models. This collaboration aims to further reduce the training costs of AI models, thereby enhancing operational efficiency. DeepSeek recently launched a new low-cost inference model that has generated considerable market excitement. To further optimize this model, DeepSeek's research team...

Rise of Domestic AI Chips! Ant Group's Training Costs Reportedly Drop 20%, Rivaling Nvidia

Bloomberg, citing sources, reports that Ant Group has made a significant breakthrough in AI, reducing AI model training costs by 20% using chips from Alibaba and Huawei. This injects new momentum into China's drive for technological self-reliance. The report further indicates that Ant Group's internal tests show these Chinese-made AI chips rival Nvidia's offerings in performance. Widespread validation and adoption of this could significantly alter the global AI landscape.

CoreWeave and OpenAI Sign $119 Billion AI Infrastructure Agreement, Deepening Partnership

GPU cloud services company CoreWeave recently announced a five-year, $119 billion strategic partnership agreement with OpenAI. Under the agreement, CoreWeave will provide OpenAI with the necessary computing power to support the training and delivery of its AI models. This significant deal marks a deepening collaboration between the two companies in the field of artificial intelligence. As part of the collaboration, CoreWeave will also issue $3...

ByteDance Open-Sources COMET: A Technology Boosting Large Model Training Efficiency by 1.7x

ByteDance's Doubao large model team recently announced a breakthrough in addressing key bottlenecks in Mixture-of-Experts (MoE) architecture, open-sourcing a significant optimization technology called COMET. This technology dramatically improves large model training efficiency, achieving a remarkable 1.7x speedup and a 40% reduction in training costs. Image Note: Image generated by AI, image licensing provider Midjourney. COMET has been deployed in ByteDance's multi-thousand-GPU cluster training, resulting in millions of GPU hours saved.

CoreWeave Acquires AI Development Platform Weights & Biases to Accelerate AI Innovation

Vivo Restructures, Establishes New AI Department and Shifts Large Model Training to On-Device

DeepSeek Open Source Release Day Four: Parallel Strategy Upgrade with DualPipe and EPLB Technologies Revolutionizes Large Model Training

Tencent Applies for Patent on 'Large Language Model Training Method' to Enhance Model Generalization and Accuracy