NVIDIA/raft

Health v2 · maintenance only

Adoption not part of the score

1043 stars · 251 forks observed · 2026-09-23

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

NVIDIA RAFT (Reusable Accelerated Functions and Tools) is a C++/Python library of CUDA-accelerated algorithms and primitives for machine learning and information retrieval. It provides building blocks such as linear algebra, sparse and dense operations, solvers, statistics, and nearest-neighbor search that underpin the RAPIDS ecosystem.

Use cases

• accelerate nearest neighbor search on gpu
• gpu-accelerated linear algebra primitives for ml
• build high performance cuda applications from reusable building blocks
• sparse matrix operations on gpu
• vector similarity search for llm applications
• multi-node multi-gpu algorithm development
• sampling and statistics computations on gpu

When to choose

• you need CUDA-accelerated primitives like ANN search, distance computations, or linear algebra in C++ or Python
• you are building GPU applications within the RAPIDS ecosystem
• you want header-only C++ libraries to reduce build and maintenance burden
• you need multi-node multi-GPU communication abstractions for distributed algorithms

When to avoid

• you need a complete end-user application rather than low-level building blocks
• your workload runs on CPU only without NVIDIA GPUs
• you need algorithms RAFT does not cover and would have to implement them yourself anyway

Facets

library · maturity active

machine-learning search-engine math gpu-computing data-science machine-learning gpu-computing data-science cpp python cross-platform cuda nearest-neighbors linear-algebra sparse-operations rapids vector-search primitives header-only search algorithms gpu linux

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