The release of PaddlePaddle Framework 3.0 features a core upgrade to unified automatic parallelism technology, aimed at simplifying the distributed training process for large models and improving development efficiency. The new version supports mixed parallelism techniques from four-dimensional to five-dimensional, employing various parallel methods such as data parallelism, tensor model parallelism, pipeline parallelism, and grouped parameter slicing parallelism, effectively enhancing the training efficiency of large models. The automatic parallelism technology uses tensor slicing syntax to automatically infer distributed slicing states and add communication operators, reducing the difficulty of development. The principles of automatic parallelism include distributed tensor representation and slicing inference.

Tencent Cloud recently launched an upgraded version of the Xing Mai Network 2.0, aiming to enhance the efficiency of large-scale model training. In the previous version, the synchronization communication time for the computation results of large models accounted for over 50% of the total, resulting in low efficiency. The new version of Xing Mai Network 2.0 has been upgraded in several aspects:1. Supports networking of up to 100,000 cards in a single cluster, doubling the scale, with a 60% improv

The American social media platform Reddit has reached an agreement with Google, valued at approximately $60 million annually. Springer Publishing Group collaborates with OpenAI, becoming the first publishing entity to deeply integrate journalism with artificial intelligence technology. OpenAI has signed a cooperation agreement with Axel Springer, indicating that future large model training may involve payments to data providers. Publishing industry companies possess a wealth of electronic graphic resources and are expected to become important datasets for large model training.

ZTE has launched the latest AI server that supports large model training aimed at the training and inference needs of small to medium-sized models. ZTE has introduced the G5 series servers and plans to release the latest AI server supporting large model training this year.

Shanghai AI Lab has released the large model training toolbox XTuner, which supports various hardware adaptations, significantly lowering the training cost barrier. XTuner is compatible with multiple open-source large models and can execute tasks such as incremental pre-training and instruction fine-tuning. XTuner balances usability and configurability with a built-in standardized process for one-click training.

The Megatron-LLaMA framework, jointly developed by Taotian Group and Aicheng Technology, aims to enhance the training performance of large language models while reducing training costs and maintaining compatibility with the LLaMA community, achieving a 176% speedup in training on 32 GPUs.

Data, as a new factor of production, is forming a data industry chain. Large model training requires massive amounts of high-quality data, which is seen as one of the core competitive advantages for future large model companies. The accounting and trading of data face many challenges, such as rights confirmation and security management.