--- title: "google-research vs raft" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/google-research-google-research-vs-nvidia-raft" tools: ["google-research-google-research", "nvidia-raft"] --- # google-research vs raft *GraphCanon updated Aug 23, 2026* ## Verdict Pick google-research if popular for its research-grade projects hosted by Google Research, this repository offers diverse machine-learning datasets and source files under specific licenses; pick raft if rAFT is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications. [google-research](https://research.google) reports 38k GitHub stars, 8.5k forks, and 2.0k open issues, last pushed Jul 30, 2026. [raft](https://docs.rapids.ai/api/raft/stable/) has 1.0k stars, 248 forks, and 446 open issues, last pushed Aug 22, 2026. Figures are from public GitHub metadata via [google-research's repository](https://github.com/google-research/google-research) and [raft's repository](https://github.com/NVIDIA/raft). | | [google-research](/tools/google-research-google-research.md) | [raft](/tools/nvidia-raft.md) | | --- | --- | --- | | Tagline | Google Research Repository | A collection of CUDA-accelerated algorithms for building high-performance machine learning and information retrieval applications. | | Stars | 38,480 | 1,036 | | Forks | 8,461 | 248 | | Open issues | 1,984 | 446 | | Language | Jupyter Notebook | Cuda | | Adopt for | Popular for its research-grade projects hosted by Google Research, this repository offers diverse machine-learning datasets and source files under specific licenses. | RAFT is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications. | | Persona | - | - | | Runtime | - | - | | License | Code is under Apache-2.0 license while datasets are released under CC BY 4.0 International license. | Apache-2.0 | | Categories | Data & Retrieval, Model Training | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [google-research](/tools/google-research-google-research.md) | [raft](/tools/nvidia-raft.md) | | --- | --- | --- | | Days since push | 6d | 1d | | Open issues (now) | 2.0k | 446 | | Stars delta | Unknown | +5 (30d) | | Open issues delta | Unknown | +2 (30d) | | Full report | [trust report](/tools/google-research-google-research/trust.md) | [trust report](/tools/nvidia-raft/trust.md) | ## Decision facts: google-research - **Requirements:** Requires a working understanding of how to navigate large repositories and selectively clone desired subdirectories. - **Adopt for:** Popular for its research-grade projects hosted by Google Research, this repository offers diverse machine-learning datasets and source files under specific licenses. - **License detail:** Code is under Apache-2.0 license while datasets are released under CC BY 4.0 International license. ## Decision facts: raft - **Requirements:** Ensure access to NVIDIA GPUs; Compatibility with CUDA is essential for utilizing the RAFT algorithms effectively.; The user must have familiarity or develop understanding of CUDA programming to optimize their application integration with RAFT. - **Adopt for:** RAFT is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications. ## Choose when ### Choose google-research if… - google-research is primarily Jupyter Notebook; raft is Cuda. - Requirements: Requires a working understanding of how to navigate large repositories and selectively clone desired subdirectories.. - Tags unique to google-research: ai, machine-learning, research. - When you need access to high-quality machine learning datasets released under the CC BY 4.0 International license alongside Apache-2.0 licensed code from Google researchers. ### Choose raft if… - raft is primarily Cuda; google-research is Jupyter Notebook. - Requirements: Ensure access to NVIDIA GPUs; Compatibility with CUDA is essential for utilizing the RAFT algorithms effectively.; The user must have familiarity or develop understanding of CUDA programming to optimize their application integration with RAFT.. - Tags unique to raft: anns, building-blocks, clustering, cuda. - - You are developing on an NVIDIA GPU architecture and require optimized, CUDA-accelerated primitives. ## When NOT to use google-research - When the need is for simpler or more streamlined datasets that match different licensing needs apart from CC BY 4.0 International and Apache-2.0. - If you require a repository with an official product-level support guarantee from Google, as this resource has explicitly declared itself as non-official. ## When NOT to use raft - - Your application does not have access to NVIDIA GPUs, as RAFT's algorithms leverage CUDA specifically for performance gains. - - If your workload requires more generalized machine learning libraries without a dependency on GPU-accelerated primitives and you are working in a multi-platform or cross-vendor environment. ## Common questions ### What is the difference between google-research and raft? google-research: Google Research Repository. raft: A collection of CUDA-accelerated algorithms for building high-performance machine learning and information retrieval applications.. See the comparison table for live GitHub stats and shared categories. ### When should I choose google-research over raft? Choose google-research over raft when google-research is primarily Jupyter Notebook; raft is Cuda; Requirements: Requires a working understanding of how to navigate large repositories and selectively clone desired subdirectories.; Tags unique to google-research: ai, machine-learning, research; When you need access to high-quality machine learning datasets released under the CC BY 4.0 International license alongside Apache-2.0 licensed code from Google researchers. ### When should I choose raft over google-research? Choose raft over google-research when raft is primarily Cuda; google-research is Jupyter Notebook; Requirements: Ensure access to NVIDIA GPUs; Compatibility with CUDA is essential for utilizing the RAFT algorithms effectively.; The user must have familiarity or develop understanding of CUDA programming to optimize their application integration with RAFT.; Tags unique to raft: anns, building-blocks, clustering, cuda; - You are developing on an NVIDIA GPU architecture and require optimized, CUDA-accelerated primitives. ### When should I avoid google-research? When the need is for simpler or more streamlined datasets that match different licensing needs apart from CC BY 4.0 International and Apache-2.0. If you require a repository with an official product-level support guarantee from Google, as this resource has explicitly declared itself as non-official. ### When should I avoid raft? - Your application does not have access to NVIDIA GPUs, as RAFT's algorithms leverage CUDA specifically for performance gains. - If your workload requires more generalized machine learning libraries without a dependency on GPU-accelerated primitives and you are working in a multi-platform or cross-vendor environment. ### Is google-research or raft more popular on GitHub? google-research has more GitHub stars (38,480 vs 1,036). Stars measure visibility, not whether either tool fits your constraints. ### Are google-research and raft open source? Yes - both are open-source projects on GitHub (google-research: Apache-2.0, raft: Apache-2.0). ### Where can I find alternatives to google-research or raft? GraphCanon lists graph-backed alternatives at [google-research alternatives](/tools/google-research-google-research/alternatives) and [raft alternatives](/tools/nvidia-raft/alternatives) ([google-research markdown twin](/tools/google-research-google-research/alternatives.md), [raft markdown twin](/tools/nvidia-raft/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/google-research-google-research-vs-nvidia-raft.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, google-research or raft? google-research: Very active. raft: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for google-research and raft? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [google-research trust report](/tools/google-research-google-research/trust); [raft trust report](/tools/nvidia-raft/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=google-research-google-research`](/api/graphcanon/graph?tool=google-research-google-research) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_