What hardware is needed for vector search at scale?

To build a vector search system that handles large-scale data, you need hardware optimized for high-speed computation, efficient memory usage, and scalable storage. The core requirements fall into three categories: processing power, memory/storage, and infrastructure for horizontal scaling. Each component plays a specific role in ensuring low-latency queries and the ability to manage billions of vectors.

First, processing power is critical for calculating vector similarities quickly. Modern GPUs (like NVIDIA A100 or H100) or specialized AI accelerators (such as Google TPUs) are often necessary because they perform parallel computations efficiently. For example, a GPU with thousands of cores can compute distances between vectors in batches, drastically reducing query times compared to CPUs. If GPUs aren’t available, multi-core CPUs with AVX-512 or SIMD instructions can still work but may require sharding data across nodes to maintain performance. Libraries like FAISS or Annoy leverage these hardware features to accelerate search, but the underlying hardware must support their requirements.

Second, memory and storage must balance speed and capacity. Vector search relies on keeping indexes in RAM for real-time performance, so systems need ample high-speed memory (e.g., DDR5 RAM) or technologies like NVMe SSDs for caching. For example, a billion 512-dimensional vectors (using 32-bit floats) require roughly 2 TB of memory. Distributed systems like Redis or in-memory databases can help, but each node needs sufficient RAM to avoid disk access, which slows queries. For cold storage, high-throughput SSDs or distributed file systems (e.g., Ceph) ensure data can be loaded quickly when scaling out.

Finally, infrastructure design determines scalability. Vector search at scale often runs on clusters, requiring fast networking (e.g., 100 GbE or InfiniBand) to minimize communication latency between nodes. Load balancers and orchestration tools (like Kubernetes) help manage traffic and node failures. For example, a Kubernetes cluster with auto-scaling can dynamically add nodes during peak loads. Cooling and power redundancy (e.g., 80+ Platinum PSUs) are also essential for data centers running these workloads 24/7. By combining these elements, developers can build systems that handle both high query volumes and large datasets efficiently.