Minglue Technology Open Sources TensorBoard.cpp to Enhance Model Training Monitoring Efficiency

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

Tencent Hunyuan Releases New Theory on Floating Point Quantization Training, Revealing the Limits of Large Model Training

In today's rapid development of Large Language Models (LLM), the training and inference costs of models have increasingly become a focus of research and application. Recently, the Tencent Hunyuan team released an important study that delves into the 'Scaling Laws' of low-bit floating point quantization training, which refers to the principles governing the scale of floating point quantization training. The core of this research lies in exploring how to significantly reduce computational and storage costs without sacrificing performance by lowering the precision of the model.

Shanghai Launches New Data Collection Fleet to Accelerate Autonomous Driving Model Training