Microsoft Open Sources FP8-LM Framework, Training Large Models 64% Faster and Saving 42% Memory Compared to BF16

Moore Threads Open-Sources Two Major AI Frameworks, Achieving Over 90% Training Efficiency on Domestic GPUs

Tsinghua Team Open-Sources Chitu Inference Engine to Boost Domestic AI Ecosystem

Recently, Professor Zhai Jidong's team from the Institute of High Performance Computing at Tsinghua University, in collaboration with Tsinghua-affiliated innovative enterprise, Qingcheng Extreme Intelligence, announced the open-sourcing of Chitu, a groundbreaking large model inference engine. This innovative technology marks another significant breakthrough for China in the AI field, particularly in inference engine development. A core highlight of the Chitu engine is its ability to natively run FP8 precision models on non-Nvidia Hopper architecture GPUs and various domestic chips.

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.

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

DeepSeek Open Source Week Day 3: Announcing DeepGEMM, an FP8 GEMM Library for AI Training and Inference

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.

"Buddhist" Optimizer C-AdamW: One Line of Code to Boost Large Model Training Speed by 1.47 Times!

ByteDance Unveils Open Source Secret Weapon HybridFlow, Boosting Large Model Training Speed by 20 Times and Slashing Costs!

Large Language Models (LLMs) like GPT and Llama have sparked a revolution in the field of artificial intelligence, but efficiently training these massive models while aligning them with human values remains a challenge. Reinforcement Learning with Human Feedback (RLHF) has become an important training method for LLMs in recent years, but traditional RLHF frameworks have limitations in flexibility, efficiency, and scalability. To address these issues, the ByteDance Doubao Large Model Team has open-sourced an RLHF framework called HybridFlow.

ByteDance Responds to 'Intern Disrupting Large Model Training': No Impact on Official Commercial Projects

Recently, ByteDance officially addressed rumors regarding an 'intern disrupting large model training'. The company confirmed that an intern did maliciously interfere with the model training tasks of the commercialization technology team's research project. However, this action did not affect the company's official commercial projects and online business, nor did it involve other large model businesses of ByteDance. Moreover, ByteDance pointed out that claims circulating online about 'involving over 8,000 nodes and causing losses of over ten million dollars' are seriously exaggerated. According to public reports, the intern was part of ByteDance's commercialization technology team.