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How volcano boosts distributed training and inference performance

· 3 min read

The Growing Demand for LLM Workloads and Associated Challenges

The increasing adoption of large language models (LLMs) has led to heightened demand for efficient AI training and inference workloads. As model size and complexity grow, distributed training and inference have become essential. However, this expansion introduces challenges in network communication, resource allocation, and fault recovery within large-scale distributed environments. These issues often create performance bottlenecks that hinder scalability.

Meet Cloud Native Batch Computing with Volcano in AI & Big Data Scenarios

· 3 min read

Cloud native batch computing engine Volcano is designed for high-performance computing applications such as AI, big data, gene sequencing, and rendering, and supports mainstream general computing frameworks. More than 58,000 global developers joined us, among whom the in-house ones come from companies such as Huawei, AWS, Baidu, Tencent, JD, and Xiaohongshu. There are 3.7k+ Stars and 800+ Forks for the project. Volcano has been proven feasible for mass data computing and analytics, such as AI, big data, and gene sequencing. Supported frameworks include Spark, Flink, TensorFlow, PyTorch, Argo, MindSpore, Paddlepaddle, Kubeflow, MPI, Horovod, MXNet, KubeGene, and Ray. The ecosystem is thriving with more developers and use cases coming up.