OceanStor AI Storage
Build a solid data foundation for AI, placing intelligence at your fingertips.
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Produits, solutions et services pour les entreprises
Build a solid data foundation for AI, placing intelligence at your fingertips.
Six Innovations
In response to the demands of large AI model training and inference, Huawei redefines distributed file storage, focusing on six key innovations to create storage systems purpose-built for AI.
Ultra Performance
Storage performance is improved 10-fold compared to conventional storage, with bandwidth in hundreds of TB/s and 100 million-level input/output operations per second (IOPS), speeding up the entire Generative AI (GenAI) process.New Data Paradigm
Cutting-edge AI data paradigms are supported, including tensors, vectors, and Key-Value (KV) cache. Retrieval-Augmented Generation (RAG) technology reduces AI hallucinations. Multi-level KV cache reduces the Time-To-First-Token (TTFT) while improving inference efficiency.Data Fabric
Storage metadata management and retrieval help achieve global data visibility and manageability, as well as data mobility that is 10 times more efficient.Scalability
Exabyte-level scale-out of a single storage cluster is supported, with each controller enclosure scaling up with Graphics Processing Units (GPUs), Data Processing Units (DPUs), or Neural Processing Units (NPUs) for near-storage computing.Data Resilience
Innovative architecture and technology achieve 99.9999% reliability. A built-in ransomware detection engine delivers 99.99% detection accuracy.Sustainability
Storage media application and system hardware innovation significantly improve storage energy efficiency and capacity density.Product
Using data-control plane separation architecture and long-term memory storage, this storage system fulfills the E2E data processing needs for AI training and inference in various industry sectors, such as financial credit, investment research, healthcare, and drug development.
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What is Storage for AI?
Storage for AI is the data storage infrastructure purpose-built for AI and Machine Learning (ML) workloads. It needs to meet the demands of high performance and scalability in hybrid workload environments, effectively manage the massive amounts of data required throughout the AI process, and ensure rapid data reads, writes, and processing. Storage systems designed for AI help training clusters improve the efficiency of data ingestion and preprocessing, increasing the utilization of computing clusters. They also enhance the cost-effectiveness and accuracy of inference applications.