Large-scale video vector search requires hardware optimized for processing, storage, and retrieval of high-dimensional data. The primary components include compute resources for encoding videos into vectors, storage systems for handling massive datasets, and memory-efficient infrastructure for fast similarity searches. Each component must scale to manage the computational and storage demands of processing thousands of video frames and their corresponding vector embeddings.
First, compute resources are critical for video encoding and search operations. Video vectorization involves extracting features from frames using deep learning models like CNNs or Vision Transformers (ViTs), which require significant GPU acceleration. For example, a system processing 100 hours of video daily might need multiple NVIDIA A100 or H100 GPUs to handle real-time inference. Distributed computing frameworks like Apache Spark or Ray can parallelize encoding across nodes, but each node still requires sufficient CPU and GPU capacity to avoid bottlenecks. Additionally, vector search itself relies on approximate nearest neighbor (ANN) algorithms, which benefit from GPU-accelerated libraries like FAISS or NVIDIA RAPIDS cuVS to speed up queries.
Second, storage systems must balance capacity, speed, and cost. Raw video files and their vector embeddings consume vast amounts of space—a single hour of 1080p video can require 5-10 GB, while vector embeddings for the same video might add 1-2 GB. High-performance SSDs or NVMe drives are essential for low-latency access to frequently queried data. For cold storage, tiered systems using object storage (e.g., AWS S3) or distributed file systems (e.g., Ceph) can reduce costs. Data redundancy and sharding are also necessary; for example, splitting vectors across multiple nodes ensures that a single drive failure doesn’t disrupt search operations.
Finally, memory and networking play a key role in query performance. ANN indexes like HNSW or IVF-PQ often reside in RAM for fast access, requiring servers with hundreds of gigabytes of memory. A cluster handling 1 billion vectors might need 10+ nodes with 256 GB RAM each, depending on vector dimensions. Networking infrastructure must minimize latency between storage and compute layers—10 Gbps or higher interconnects are standard. Load balancers and caching layers (e.g., Redis) can further optimize throughput. For example, caching frequently accessed vectors in-memory reduces disk I/O, ensuring sub-millisecond response times for common queries.