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How HNSW and IVF find neighbors
HNSW: navigate a graph
Hierarchical Navigable Small World (HNSW) connects vectors in a layered graph. At query time, the search follows links toward likely neighbors instead of comparing the query with every vector. The links add memory overhead, while the graph enables approximate search. The foundational HNSW paper is by Malkov and colleagues: arXiv:1603.09320.
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IVF: probe selected partitions
An inverted-file index (IVF) assigns vectors to coarse clusters, then searches selected partitions for each query. IVF is a family of configurations, not a single memory or performance profile: IVF-Flat retains full-precision vectors, while IVF-SQ and IVF-PQ compress their representations with corresponding accuracy trade-offs.
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HNSW and IVF are not always mutually exclusive. FAISS’s large-scale indexing guide compares IVF configurations that use HNSW as a coarse quantizer alongside other quantizer approaches: FAISS: indexes that do not fit in RAM.
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Compare the trade-offs that affect your workload
| Decision factor | HNSW | IVF |
|---|---|---|
| Search structure | Layered graph; query search navigates links between vectors. | Coarse clusters and inverted lists; query search visits selected partitions. |
| Memory | Stores vectors plus graph links; more links can use more RAM. | IVF-Flat retains full vectors and partition metadata. Quantized variants store compact representations. |
| Recall and latency controls in FAISS | efSearch adjusts graph-search effort; more effort can improve search quality at a speed cost. |
nprobe controls how many partitions are searched; probing more increases work and can improve recall. |
| Compression | The graph itself does not provide the same vector compression as IVF-PQ; compression is a separate design choice. | IVF-SQ and IVF-PQ reduce representation size; PQ compresses more aggressively and needs more tuning. |
| Build and training | FAISS says HNSW needs no training, but graph construction can be expensive. | Requires clustering; IVF-PQ also trains codebooks. |
| Potential fit | Worth benchmarking when the index fits in RAM and high-quality CPU search is important. | Worth benchmarking when partitioned search or a smaller footprint matters; IVF-PQ is relevant when index size is the main constraint. |
These are family-level distinctions; details depend on the library and configuration. For example, FAISS gives this memory model for its stated HNSW representation: (d * 4 + M * 2 * 4) bytes per vector, where d is vector dimension and M controls graph links. It is a model for understanding link overhead, not a universal memory estimate for all implementations. FAISS describes an M range of 4–64 and notes that higher values use more RAM. See FAISS’s index-selection guidance.
Which should you try first?
When the index fits in RAM
Start by benchmarking HNSW if CPU search quality is a priority and the full index fits in available memory. Tune efSearch against the recall and latency you need rather than assuming one setting is best. NVIDIA cuVS likewise recommends HNSW for high-quality CPU search when the index fits in memory: cuVS indexing guide.
When memory is constrained but vectors should remain full precision
Benchmark IVF-Flat. It retains full-precision vectors while using selected partitions to reduce the amount searched per query. In FAISS, nprobe controls how many partitions are examined, creating a workload-dependent recall and latency trade-off.
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Benchmark quantized IVF. cuVS characterizes IVF-SQ as offering a smaller recall trade-off and IVF-PQ as more aggressively compressed, with a larger accuracy trade-off and potentially a need for refinement or reranking. Measure the recall loss and any reranking cost on your own workload before accepting compression.
When exact neighbors are required
Include a flat, exhaustive index as a baseline. FAISS says its Flat indexes are the only ones that guarantee exact results and recommends them as a baseline for approximate indexes. They may require more search work, but they provide a reference for measuring approximate recall. See FAISS’s index-selection guidance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to benchmark fairly
- Fix the workload: Use representative queries and keep vector dimensions, distance metric, dataset size and distribution, hardware, and filters consistent between candidates.
- Set the quality target: Measure recall@k against an exact baseline, then compare latency at a matched recall target. A latency result without its recall level can be misleading.
- Measure the serving profile: Record p50, p95, and p99 query latency, throughput under expected concurrency, and peak resident memory.
- Include index lifecycle costs: Record build time, any IVF training cost, resource use during construction, and behavior under the expected update pattern.
- Tune the relevant controls: Sweep HNSW’s
efSearchor IVF’snprobe; for compressed IVF, also evaluate the accuracy and cost of refinement or reranking where used.
Benchmark numbers are tied to their setup. For example, FAISS’s large-scale indexing guide reports one IVF operating point—nprobe=128 and quantizer_efSearch=32—with recall@1 of 0.6786 and 0.05387 ms/query in that guide’s particular experiment. It describes a normalized 2.2 GHz Xeon E5-2698, 80-core platform, with experiments run using 32 cores. Those figures illustrate a documented configuration, not a general IVF-versus-HNSW speed or recall result: FAISS’s benchmark setup and results.
What the published guidance can—and cannot—settle
FAISS’s index-selection guidance favors HNSW when RAM is plentiful or the dataset is small, and describes IVF variants as options when memory matters. NVIDIA cuVS points to IVF-Flat when partitioning should retain full-precision vectors, and IVF-PQ when index size is the main bottleneck and added tuning or reranking is acceptable. These are useful starting points from specific implementations, not universal performance guarantees: FAISS guidance and NVIDIA cuVS guidance.
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