faiss-gpu
A library for efficient similarity search and clustering of dense vectors (GPU support).
Decision gist · record as of 2026-08-14
Yes, if you have an NVIDIA GPU and need fast similarity search or clustering on dense vectors. The library is production-stable, actively maintained, MIT-licensed, and has no known vulnerabilities. Install friction is moderate due to CUDA dependencies, but the performance gains for GPU-accelerated workloads justify the setup. Not suitable for CPU-only environments.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires NVIDIA GPU with CUDA 12 support and nvidia-cuda-runtime-cu12, nvidia-cublas-cu12, nvidia-curand-cu12 installed; Linux x86_64 only.
- Medium install friction due to GPU dependencies: requires nvidia-cuda-runtime-cu12, nvidia-cublas-cu12, and nvidia-curand-cu12.
- Actively maintained with a recent release and strong community engagement (40741 stars).
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions—you may use, modify, and distribute faiss-gpu freely as long as you include the license notice.
last release 2026-08-03 (11 days) · last repo commit 2026-08-13 · 40,741 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 384,820 downloads/mo, #7,065 on PyPI
Alternatives
Verify before relying
pip install faiss-gpu
import faiss
import numpy as np
index = faiss.IndexFlatL2(d)
index.add(vectors)
distances, indices = index.search(query_vectors, k)- Whether the package supports AMD ROCm as an alternative to CUDA (mentioned in description but not in runtime deps).
- Specific performance benchmarks or latency expectations for typical workloads.
- Memory overhead per index vector for different index types.
What it is and what it does
Faiss is a C++ library with Python bindings for efficient similarity search and vector clustering. It handles both exact and approximate nearest-neighbor queries on dense vectors, with GPU acceleration for speed-critical operations. The library trades off search quality, memory usage, training time, and query latency depending on the index type chosen—from simple baselines like flat L2 search to compressed quantization codes that scale to billions of vectors.
The GPU implementation accepts input from CPU or GPU memory and handles transfers automatically, making GPU indexes drop-in replacements for CPU equivalents. It supports L2 distance, dot product, and cosine similarity comparisons. The package is production-stable, actively maintained by Meta's AI Research group, and widely used in machine learning and information retrieval pipelines.
Use it for
- Build a semantic search engine over embeddings from a language model by indexing vectors and querying for nearest matches.
- Cluster high-dimensional data (e.g., image embeddings) using Faiss's k-means or other clustering algorithms.
- Implement approximate nearest-neighbor search for recommendation systems handling large candidate item sets.
- Scale vector search to billions of items by using compressed quantization indexes that fit in GPU memory.
- Benchmark and tune index parameters for production similarity search workloads.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have an NVIDIA GPU and need fast similarity search or clustering on dense vectors.
The library is production-stable, actively maintained, MIT-licensed, and has no known vulnerabilities. Install friction is moderate due to CUDA dependencies, but the performance gains for GPU-accelerated workloads justify the setup. Not suitable for CPU-only environments.
Install
faiss-gpu on PyPI
Before you install
Medium install friction due to GPU dependencies: requires nvidia-cuda-runtime-cu12, nvidia-cublas-cu12, and nvidia-curand-cu12. Actively maintained with a recent release and strong community engagement (40741 stars).
Requires NVIDIA GPU with CUDA 12 support and nvidia-cuda-runtime-cu12, nvidia-cublas-cu12, nvidia-curand-cu12 installed; Linux x86_64 only.
License in practice
MIT license permits commercial and private use with minimal restrictions—you may use, modify, and distribute faiss-gpu freely as long as you include the license notice.
Quickstart
pip install faiss-gpu
import faiss
import numpy as np
index = faiss.IndexFlatL2(d)
index.add(vectors)
distances, indices = index.search(query_vectors, k)Verify before relying
- Whether the package supports AMD ROCm as an alternative to CUDA (mentioned in description but not in runtime deps).
- Specific performance benchmarks or latency expectations for typical workloads.
- Memory overhead per index vector for different index types.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 5 packagesnumpypackagingnvidia-cuda-runtime-cu12nvidia-cublas-cu12nvidia-curand-cu12 |
| Maintenance | Actively maintained 11 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 384,820 / month, #7,065 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: GPU :: NVIDIA CUDA :: 12Intended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: POSIX :: LinuxProgramming Language :: C++Programming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: CPythonTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software Development :: Libraries :: Python Modules |
Evidence: faiss_gpu-1.15.0-cp310-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
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See also libcuvs-cu12 · usearch · cuvs-cu12 · simsimd · llama-index-vector-stores-faiss · fastcluster · annoy · libcuml-cu12 · gensim · pagefind-bin