GPU-Accelerated Vector Search for High-Throughput vs CPU Vector Search for High-Throughput

GPU-accelerated vector search excels at massively parallel query execution because its architecture can process thousands of similarity calculations simultaneously. For example, a single NVIDIA H100 GPU using the CAGRA algorithm can exceed 100,000 queries per second (QPS) on billion-scale datasets, a throughput level that would require a large cluster of CPU nodes to match. This raw throughput makes GPUs the default choice for online inference services where a single, high-traffic model serves millions of users.

CPU-based vector search takes a fundamentally different approach by leveraging optimized graph traversal and SIMD (AVX-512) instructions to minimize per-query latency without the kernel launch overhead inherent to GPU computing. A well-tuned CPU-based HNSW index can achieve sub-millisecond p99 latency for a single query, making it highly efficient for workloads with lower, more consistent concurrency. This results in a superior performance-per-dollar ratio when the system is not saturated with thousands of simultaneous requests, as CPU instances are generally cheaper and more readily available.

The key trade-off: If your priority is maximizing raw throughput (QPS) for a heavily loaded, centralized inference service, choose GPU-accelerated search. The parallel architecture directly translates to handling massive query volumes from a smaller hardware footprint. If you prioritize cost-efficiency under moderate load, simpler infrastructure, or have a microservice architecture with many less-saturated endpoints, choose CPU-based search. The decision hinges on whether your bottleneck is total system throughput or the cost of idle, high-powered hardware.

GPU-accelerated vector search excels at raw, brute-force throughput for massive concurrent query loads because its thousands of cores perform distance calculations in parallel. For example, a single NVIDIA H100 using the CAGRA algorithm can sustain over 500,000 queries per second on the Deep-1B dataset, a throughput level that would require a large cluster of CPU nodes to match. This makes GPUs the undisputed champion for services where peak queries per second (QPS) is the primary metric and cost is a secondary concern.

CPU vector search takes a different approach by leveraging sophisticated graph-based indexing like HNSW and hardware-optimized SIMD instructions to minimize the work done per query. This results in a superior performance-per-dollar ratio for stable, predictable workloads. A well-tuned CPU instance can deliver p99 latency under 10ms for millions of vectors at a fraction of the cloud cost of a GPU instance, making it the pragmatic choice when throughput demands don't require a GPU's parallel firepower.

The key trade-off is between peak throughput and cost-efficiency. GPU architectures provide an unparalleled ceiling for QPS, making them essential for 'bursty' inference services or billion-scale deployments where latency must remain flat. However, they introduce higher idle costs and kernel launch overhead that can negate their advantage at low query volumes. CPU systems, conversely, offer a lower, more predictable cost curve and simpler operational management, which is ideal for applications with consistent, moderate traffic.

The decision hinges on your workload's shape, not just its size. If your service experiences unpredictable spikes from millions of users and you need to guarantee a p99 latency under 5ms, the GPU's parallel architecture is non-negotiable. Choose a GPU-native library like RAFT or cuVS. If your throughput is high but predictable and you prioritize minimizing cloud spend, a horizontally scalable CPU cluster using an optimized HNSW index in a database like Qdrant or Milvus will provide the best total cost of ownership. Consider GPU for peak capacity and CPU for steady-state efficiency.