Core Viewpoint - The primary challenge for storage vendors is how to store data for artificial intelligence (AI) access, ensuring that AI models and agents can quickly retrieve this data through efficient data pipelines [1][3]. Group 1: AI Integration in Storage - AI is being utilized in storage management to enhance efficiency and is crucial for cybersecurity [1]. - Storage hardware and software vendors are adopting Nvidia GPUDirect support to expedite raw data transmission to GPUs, which has expanded from file support to include object storage via RDMA [3][4]. - Data management software can transition from storage array controllers to databases or data lakes, and can be hosted in public clouds like AWS, Azure, or GCP [3][4]. Group 2: Data Processing and Storage Solutions - Data must be identified, located, selected, and vectorized before being usable by large language models (LLMs), with vector storage options including specialized vector databases [4][5]. - Vendors like VAST Data are developing their own AI pipelines, contrasting with companies like Qumulo that focus on internal operations enhancement without GPUDirect support [5][10]. - Major storage vendors such as Cloudian, Dell, and IBM support GPUDirect for file and object storage, although support may vary across product lines [8][9]. Group 3: Advanced AI Capabilities - Nvidia's BasePOD and SuperPOD GPU server systems have been certified by several vendors, indicating a trend towards deeper integration with Nvidia's AI software [9][10]. - Companies like Hammerspace and VAST Data support Nvidia GPU server's key-value (KV) cache offloading, which is essential for optimizing AI model performance [11]. - Cloud file service providers are also exploring AI data pipelines to support GPU-based inference, although collaboration with Nvidia remains limited [12]. Group 4: Challenges in Data Accessibility - Backup and archive data pose challenges for AI model access, as many backup vendors are reluctant to provide API access to their stored data [13][14]. - Organizations with diverse storage vendors and systems may face difficulties in creating a unified strategy for AI model data accessibility, potentially leading to vendor consolidation [14].