Delegate on-premises AI data storage tasks to QNAP NAS

 

Data management and storage are keys to successful AI

According to market research firm Gartner, by 2026, more generative AI resources will move from the cloud to endpoint and edge devices. The popular AI model RAG (Retrieval Augmented Generation) relies more on retrieving information from large databases, making efficient and reliable edge data storage the first step for enterprises to successfully adopt generative AI.

Create a high-reliability on-premises AI storage database with QNAP NAS

High-performance, large-capacity QNAP NAS is suitable for storing raw data and can serve as storage/backup servers for RAG.

Scenario 1Cross-platform storage of large raw data

QNAP NAS enables seamless access between local and cloud storage, making it ideal for storing raw data, videos, and photos from various platforms.

• Supports S3 Compatible Object Storage, allowing migration of cloud-stored data to NAS

• Supports native Samba, NFS protocols for seamless data access and sharing across Windows, Linux, macOS, and other platforms

• Supports WORM, powerful data search, RAID data protection, and permission control to prevent unauthorized data modification, ensuring data integrity and consistency

Scenario 2Storage/Backup Server for RAG

QNAP NAS provides petabyte-scale storage potential, advanced snapshot and backup technologies, and all-flash arrays, meeting the stringent requirements for frequent data access and processing in RAG.

• The industry's most complete all-flash NAS product lineup provides greater options and flexibility 

• Supports 25/100GbE high-speed networks to unleash the full potential of all-flash NAS for fast data retrieval with high IOPS and low latency

• Natively supports iSCSI / Samba protocols to mount NAS storage space for AI computing servers or other storage devices

• Automatically backs up vector data to QNAP NAS S3 Object Storage Space on a regular basis, simplifying data backup and restoration

• Supports container technologies to accelerate the deployment and management of vector databases