The performance of a vector database is heavily influenced by the hardware it runs on because operations like similarity search, indexing, and vector computations are resource-intensive. CPU cache size, RAM speed, and GPU acceleration directly impact how efficiently these operations execute, which in turn affects benchmark results like query latency, throughput, and indexing speed.
CPU Cache Sizes Vector databases rely on fast access to data during operations like nearest-neighbor search. Larger CPU caches reduce the need to fetch data from slower RAM, which is critical for latency-sensitive tasks. For example, during a k-NN search, the CPU repeatedly compares query vectors with indexed vectors. If the indexed data fits in the CPU’s L2 or L3 cache, these comparisons execute much faster than if they require fetching from main memory. A small cache forces the CPU to wait for RAM, increasing query latency. Benchmarks often show significant differences between CPUs with similar clock speeds but varying cache sizes—e.g., a CPU with 32MB L3 cache might handle 1M vectors with 30% lower latency than one with 16MB L3 cache, assuming the dataset partially fits in the larger cache.
RAM Speed and Bandwidth Vector databases often store indices (e.g., HNSW graphs) in memory, and RAM speed determines how quickly these structures can be accessed. Faster RAM (e.g., DDR5 vs. DDR4) reduces latency for fetching vector data during searches. Bandwidth matters during bulk operations like index construction or batch queries. For instance, building a hierarchical navigable small-world (HNSW) index involves frequent random memory accesses, and higher RAM bandwidth (e.g., 4800 MT/s vs. 3200 MT/s) can reduce build times by 15–20% in benchmarks. However, if the working set exceeds available RAM, forcing disk swaps, performance degrades dramatically—this is why most vector DBs recommend ample memory.
GPU Acceleration GPUs excel at parallelizing vector operations, making them ideal for tasks like similarity search across large batches. A GPU’s thousands of cores can process many vector comparisons simultaneously, drastically improving throughput. For example, a benchmark using GPU-accelerated FAISS might achieve 10x higher queries per second (QPS) compared to a CPU-only setup when handling 100K-dimensional vectors. However, GPUs require data to be transferred from CPU memory, adding overhead. This makes them better suited for batch processing (e.g., offline indexing) than low-latency real-time queries unless the entire dataset resides in GPU memory. Additionally, not all vector DB algorithms are GPU-optimized—some ANN methods, like tree-based indices, see less benefit from GPUs compared to brute-force or product quantization approaches.
In summary, hardware choices dictate trade-offs between latency, throughput, and scalability. A CPU with a large cache and fast RAM benefits low-latency single-query workloads, while GPUs shine in high-throughput batch scenarios. Benchmarks must account for these factors to avoid misleading comparisons—e.g., a GPU-optimized benchmark might mask poor CPU performance, or vice versa. Developers should align hardware with their workload: prioritize cache/RAM for real-time applications and GPUs for large-scale batch processing.