--- title: "DeepSeek-R1 vs raft" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepseek-ai-deepseek-r1-vs-nvidia-raft" tools: ["deepseek-ai-deepseek-r1", "nvidia-raft"] --- # DeepSeek-R1 vs raft *GraphCanon updated Aug 23, 2026* ## Verdict Pick DeepSeek-R1 if deepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use; pick raft if rAFT is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications. [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) reports 92k GitHub stars, 12k forks, and 38 open issues, last pushed Jun 27, 2025. [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 [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1) and [raft's repository](https://github.com/NVIDIA/raft). | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [raft](/tools/nvidia-raft.md) | | --- | --- | --- | | Tagline | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | A collection of CUDA-accelerated algorithms for building high-performance machine learning and information retrieval applications. | | Stars | 91,982 | 1,036 | | Forks | 11,706 | 248 | | Open issues | 38 | 446 | | Language | - | Cuda | | Adopt for | DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. | RAFT is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | LLM Frameworks, Model Training | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [raft](/tools/nvidia-raft.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 405d | 1d | | Open issues (now) | 38 | 446 | | Stars delta | Unknown | +5 (30d) | | Open issues delta | Unknown | +2 (30d) | | Full report | [trust report](/tools/deepseek-ai-deepseek-r1/trust.md) | [trust report](/tools/nvidia-raft/trust.md) | ## Decision facts: DeepSeek-R1 - **Pricing:** freemium - The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository. - **Requirements:** Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs. - **Adopt for:** DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. ## 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 DeepSeek-R1 if… - License: DeepSeek-R1 is MIT, raft is Apache-2.0. - Pricing: The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository.. - Requirements: Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs.. - Tags unique to DeepSeek-R1: commercial use, derived models, distilled models, mit-license. - Also covers LLM Frameworks. - When you need to work with pre-trained models derived specifically from the Qwen-2.5 and Llama3.x series, benefiting from their unique characteristics. ### Choose raft if… - License: raft is Apache-2.0, DeepSeek-R1 is MIT. - 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. - Also covers Data & Retrieval. - - You are developing on an NVIDIA GPU architecture and require optimized, CUDA-accelerated primitives. ## When NOT to use DeepSeek-R1 - Avoid if you need foundational models rather than distilled versions, as DeepSeek-R1 specializes in providing smaller, more efficient models suitable for resource-constrained environments. - If your project is tightly regulated or requires models from a different lineage, as DeepSeek-R1 exclusively provides derivatives of Qwen and LLaMA series. ## 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 DeepSeek-R1 and raft? DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. 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 DeepSeek-R1 over raft? Choose DeepSeek-R1 over raft when License: DeepSeek-R1 is MIT, raft is Apache-2.0; Pricing: The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository.; Requirements: Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs.; Tags unique to DeepSeek-R1: commercial use, derived models, distilled models, mit-license; Also covers LLM Frameworks; When you need to work with pre-trained models derived specifically from the Qwen-2.5 and Llama3.x series, benefiting from their unique characteristics. ### When should I choose raft over DeepSeek-R1? Choose raft over DeepSeek-R1 when License: raft is Apache-2.0, DeepSeek-R1 is MIT; 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; Also covers Data & Retrieval; - You are developing on an NVIDIA GPU architecture and require optimized, CUDA-accelerated primitives. ### When should I avoid DeepSeek-R1? Avoid if you need foundational models rather than distilled versions, as DeepSeek-R1 specializes in providing smaller, more efficient models suitable for resource-constrained environments. If your project is tightly regulated or requires models from a different lineage, as DeepSeek-R1 exclusively provides derivatives of Qwen and LLaMA series. ### 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 DeepSeek-R1 or raft more popular on GitHub? DeepSeek-R1 has more GitHub stars (91,982 vs 1,036). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSeek-R1 and raft open source? Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, raft: Apache-2.0). ### Where can I find alternatives to DeepSeek-R1 or raft? GraphCanon lists graph-backed alternatives at [DeepSeek-R1 alternatives](/tools/deepseek-ai-deepseek-r1/alternatives) and [raft alternatives](/tools/nvidia-raft/alternatives) ([DeepSeek-R1 markdown twin](/tools/deepseek-ai-deepseek-r1/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/deepseek-ai-deepseek-r1-vs-nvidia-raft.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepSeek-R1 or raft? DeepSeek-R1: Dormant. 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 DeepSeek-R1 and raft? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSeek-R1 trust report](/tools/deepseek-ai-deepseek-r1/trust); [raft trust report](/tools/nvidia-raft/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=deepseek-ai-deepseek-r1`](/api/graphcanon/graph?tool=deepseek-ai-deepseek-r1) - 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/_