Large-model multimodal data introduces several new requirements for storage protocols due to its unique characteristics, including massive volume, diverse data types, high throughput demands, and low-latency access needs. Below are the key requirements and explanations with examples:
Multimodal data (e.g., text, images, audio, video, and 3D models) used in large models often involves terabytes or even petabytes of data. Storage protocols must support high throughput to handle concurrent read/write operations efficiently. For example, training a vision-language model requires loading millions of high-resolution images and corresponding text captions simultaneously.
Example: A storage protocol like NVMe over Fabrics (NVMe-oF) or parallel file systems (e.g., Lustre, GPFS) is preferred to deliver gigabytes per second (GB/s) throughput.
Large models rely on fast data retrieval to minimize training and inference delays. Storage protocols must ensure low-latency access to small files (e.g., metadata) and large files (e.g., video streams).
Example: Object storage with S3-compatible APIs optimized for low-latency metadata operations can improve efficiency. Tencent Cloud’s COS (Cloud Object Storage) provides high-performance access with millisecond-level latency.
Multimodal datasets grow continuously, requiring storage protocols that scale seamlessly horizontally. Traditional protocols like NFS may struggle with petabyte-scale data.
Example: Distributed storage systems with elastic scaling (e.g., Ceph, HDFS) or cloud-native object storage (e.g., Tencent Cloud COS) can handle dynamic capacity expansion.
Multimodal data includes structured (e.g., JSON annotations) and unstructured data (e.g., raw images, videos). Protocols must efficiently manage both.
Example: A hybrid storage approach combining block storage (for structured data) and object storage (for unstructured data) ensures optimal performance. Tencent Cloud offers CBS (Cloud Block Storage) and COS for such use cases.
Training large models requires high data reliability. Protocols must ensure data integrity and availability, even during hardware failures.
Example: Erasure coding and multi-AZ replication (e.g., Tencent Cloud COS’s cross-region replication) enhance durability.
Multimodal datasets have complex metadata (e.g., labels, timestamps). Storage protocols must optimize metadata access to avoid bottlenecks.
Example: Metadata indexing in object storage (e.g., Tencent Cloud COS’s hierarchical namespace) accelerates data retrieval.
Frameworks like PyTorch or TensorFlow require storage protocols that integrate well with distributed data loading.
Example: Protocols supporting parallel data access (e.g., TensorFlow’s TFRecord with optimized storage backends) improve training efficiency.
Tencent Cloud Recommendation:
For large-model multimodal data, Tencent Cloud’s COS (Cloud Object Storage) provides high throughput, low latency, and scalability, while CBS (Cloud Block Storage) ensures low-latency access for structured data. Additionally, Tencent Cloud’s High-Performance Computing (HPC) solutions optimize storage protocols for AI/ML workloads.
These requirements ensure that storage systems can meet the demanding needs of large-model multimodal data processing.