Can Faiss Run on a GPU?

When working with large-scale vector similarity search, performance and scalability become crucial. That’s where Faiss, an open-source library developed by Facebook AI Research, stands out. Designed for efficient similarity search and clustering of dense vectors, Faiss is widely adopted in applications like recommendation systems, image retrieval, semantic search, and large language model (LLM) embeddings. But a common question developers and data scientists ask is: Can Faiss run on a GPU?

The answer is yes—Faiss can run on a GPU, and doing so significantly accelerates performance, especially when dealing with millions or even billions of high-dimensional vectors. In this comprehensive article, we’ll explore how Faiss leverages GPU acceleration, what’s required to set it up, its architecture, use cases, and benchmarks that show why GPU support is critical in modern AI pipelines.

What Is Faiss?

Faiss (Facebook AI Similarity Search) is a high-performance library built in C++ with Python bindings. It is designed for:

• Nearest Neighbor Search (NNS)
• Clustering
• Similarity computation on dense vectors

Faiss supports both exact and approximate nearest neighbor (ANN) search and is optimized to handle large datasets that cannot fit in memory.

CPU vs. GPU: Why It Matters

While Faiss can run on CPUs, using CPUs for large-scale similarity search is often computationally expensive and time-consuming.

CPU Limitations

• Slower matrix multiplication and distance computation
• Memory constraints when handling large indexes
• Longer training and search time for high-dimensional data

GPU Advantages

• Parallel computation for faster distance calculations
• Higher throughput using thousands of CUDA cores
• Capability to fit larger indices in GPU memory
• Ideal for real-time inference scenarios

How Faiss Utilizes the GPU

Faiss supports GPU acceleration through a CUDA-enabled backend, which is integrated as a separate module within the Faiss codebase. This module is designed specifically to offload compute-intensive operations from the CPU to the GPU, taking advantage of the thousands of parallel threads that modern NVIDIA GPUs offer. The primary benefit of running Faiss on a GPU is the massive speedup in training and querying indexes, especially for high-dimensional vectors and large-scale datasets.

The GPU version of Faiss enables both exact and approximate nearest neighbor (ANN) searches, supporting a range of indexing structures that have been optimized for GPU performance. This is particularly valuable for workloads in which latency and throughput are critical, such as real-time recommendation systems, semantic search engines, or large language model (LLM) embedding retrieval pipelines.

Under the hood, Faiss uses the cuBLAS and cuRAND libraries—optimized by NVIDIA—to handle fast matrix multiplication and random number generation on the GPU. These operations are the backbone of many similarity computations, including L2 distance and inner product calculations. Faiss also implements specialized CUDA kernels for building and searching indexes, such as IVF (Inverted File Index), PQ (Product Quantization), and HNSW (Hierarchical Navigable Small World Graphs).

One of the most useful features of GPU Faiss is the ability to move CPU indexes into GPU memory using faiss.index_cpu_to_gpu, allowing users to continue using familiar APIs while gaining performance benefits. Developers can create a CPU index, train it, and then transfer it to GPU for fast querying. Alternatively, they can initialize and train indexes entirely on GPU to eliminate data transfer overhead.

For memory management, Faiss allows the allocation of GPU resources via StandardGpuResources, which handles temporary memory allocations and stream synchronization. This gives users finer control over resource usage and can be especially helpful when running multiple concurrent Faiss jobs on a single GPU. Additionally, Faiss offers batched processing for both training and searching to help manage memory consumption and improve throughput.

Here are some GPU-compatible indexes and their key use cases:

• IndexFlatL2: Performs brute-force L2 distance search and is useful for benchmarking or small datasets.
• IndexIVFFlat: A combination of an inverted index and a flat quantizer; it is good for approximate searches on medium to large datasets.
• IndexIVFPQ: Adds product quantization on top of IVF, significantly reducing memory usage while maintaining reasonable accuracy.
• IndexHNSW: Suitable for graph-based ANN search, enabling high recall with minimal latency.

Each of these indexes is optimized to run on GPU and can be combined with quantization techniques to strike a balance between memory efficiency and search quality.

In practice, the GPU acceleration in Faiss can reduce query latency from hundreds of milliseconds to under 10 milliseconds, depending on the index type and dataset size. This makes Faiss a strong candidate for deployment in latency-sensitive environments like online recommendations or voice search systems.

Moreover, Faiss on GPU supports multi-GPU setups, enabling horizontal scaling for even larger datasets. Using features like index_cpu_to_all_gpus or distributing partitions manually, developers can run vector search across several GPUs in parallel. This is particularly beneficial for enterprise-scale use cases where real-time performance across billions of vectors is required.

To summarize, Faiss utilizes the GPU by:

• Offloading similarity calculations to CUDA-accelerated functions
• Supporting a wide range of indexing structures for various data sizes and precision requirements
• Allowing flexible index transfers between CPU and GPU
• Providing utilities for memory management and batch processing
• Supporting distributed and multi-GPU indexing for large-scale deployments

Overall, the GPU capabilities in Faiss transform it from a powerful research tool into a production-grade solution for scalable and high-performance vector search.

Installing Faiss with GPU Support

To use Faiss with GPU acceleration, you need to install the GPU version of the library. The easiest way is via conda or building from source:

Using Conda

conda install -c pytorch faiss-gpu

This automatically installs the GPU-accelerated version compatible with your PyTorch and CUDA versions.

From Source

1. Ensure you have a CUDA toolkit installed (>=10.2 recommended).
2. Clone the Faiss repository:

git clone https://github.com/facebookresearch/faiss.git
cd faiss

3. Build with GPU support:

cmake -B build -DFAISS_ENABLE_GPU=ON .
make -C build -j

How to Use Faiss on GPU

Here is a basic Python example:

import faiss
import numpy as np

# Set GPU device
res = faiss.StandardGpuResources()

# Generate random vectors
d = 128
nb = 100000
xb = np.random.random((nb, d)).astype('float32')

# Create CPU index and move to GPU
cpu_index = faiss.IndexFlatL2(d)
gpu_index = faiss.index_cpu_to_gpu(res, 0, cpu_index)

# Add vectors and perform search
gpu_index.add(xb)
xq = np.random.random((10, d)).astype('float32')
dists, indices = gpu_index.search(xq, k=5)
print(indices)

Use Cases for GPU-Accelerated Faiss

1. Large-Scale Semantic Search

Companies like Google, Meta, and OpenAI use Faiss for embedding-based retrieval in systems like vector search engines or retrieval-augmented generation (RAG) pipelines.

2. Image and Video Search

Visual embeddings extracted from CNNs or Vision Transformers can be indexed with Faiss to enable real-time similarity matching.

3. Recommendation Engines

Vector-based product recommendations are powered by Faiss to find similar user profiles or items efficiently.

4. LLM Embedding Search

For storing and querying embeddings generated by models like OpenAI’s Ada, BERT, or Sentence Transformers, Faiss on GPU can reduce latency significantly.

Performance Benchmarks

Running Faiss on GPU can result in massive performance gains:

This speedup is critical for real-time applications, especially when serving AI models in production environments.

Best Practices

• Batching: Use batching for adding and querying vectors to avoid GPU memory overflow.
• Memory Management: Monitor GPU memory usage with nvidia-smi or similar tools.
• Index Training: Pre-train complex indexes (e.g., IVFPQ) on GPU for faster convergence.
• Index Persistence: Save and load GPU indexes using Faiss serialization to optimize startup time.

Limitations and Considerations

While GPU acceleration provides significant benefits, there are trade-offs:

• GPU Memory Limitations: Large indexes may not fit entirely in GPU memory and require sharding.
• Deployment Complexity: Requires compatible CUDA setup and sometimes custom Docker environments.
• Debugging: GPU stack traces can be harder to debug compared to CPU.

Alternative GPU-Accelerated Libraries

Although Faiss is one of the most popular tools for vector search, others include:

• ScaNN (by Google): Focused on scalable vector search.
• Milvus: A cloud-native vector database with GPU support.
• Weaviate: Vector database with hybrid search capabilities.
• Annoy: Lightweight, but CPU-only.

Conclusion

So, can Faiss run on a GPU? Absolutely—and it should when performance and scale are critical. GPU support in Faiss unlocks fast, efficient, and scalable vector similarity search for applications in NLP, computer vision, recommender systems, and more. By leveraging NVIDIA GPUs and CUDA, Faiss can handle billions of vectors with low latency and high throughput, making it an essential tool in the modern AI engineer’s toolbox.