--- title: "ColossalAI vs raft" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hpcaitech-colossalai-vs-nvidia-raft" tools: ["hpcaitech-colossalai", "nvidia-raft"] --- # ColossalAI vs raft *GraphCanon updated Aug 23, 2026* ## Verdict Pick ColossalAI if colossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models; pick raft if rAFT is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications. [ColossalAI](https://www.colossalai.org) reports 41k GitHub stars, 4.5k forks, and 505 open issues, last pushed Jul 13, 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 [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI) and [raft's repository](https://github.com/NVIDIA/raft). | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [raft](/tools/nvidia-raft.md) | | --- | --- | --- | | Tagline | Making large AI models cheaper, faster and more accessible | A collection of CUDA-accelerated algorithms for building high-performance machine learning and information retrieval applications. | | Stars | 41,432 | 1,036 | | Forks | 4,506 | 248 | | Open issues | 505 | 446 | | Language | Python | Cuda | | Adopt for | ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models. | RAFT is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Inference & Serving, Model Training | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [raft](/tools/nvidia-raft.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 24d | 1d | | Open issues (now) | 505 | 446 | | Stars delta | Unknown | +5 (30d) | | Open issues delta | Unknown | +2 (30d) | | Full report | [trust report](/tools/hpcaitech-colossalai/trust.md) | [trust report](/tools/nvidia-raft/trust.md) | ## Shared compatibility - **Python**: [ColossalAI](/tools/hpcaitech-colossalai.md) - Python runtime; [raft](/tools/nvidia-raft.md) - Python runtime ## Decision facts: ColossalAI - **Adopt for:** ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models. ## 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 ColossalAI if… - ColossalAI is primarily Python; raft is Cuda. - Tags unique to ColossalAI: ai, big model, data-parallelism, deep-learning. - Also covers Inference & Serving. - You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### Choose raft if… - raft is primarily Cuda; ColossalAI is Python. - 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 ColossalAI - You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems. - Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series). - You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7. ## 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 ColossalAI and raft? ColossalAI: Making large AI models cheaper, faster and more accessible. 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 ColossalAI over raft? Choose ColossalAI over raft when ColossalAI is primarily Python; raft is Cuda; Tags unique to ColossalAI: ai, big model, data-parallelism, deep-learning; Also covers Inference & Serving; You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### When should I choose raft over ColossalAI? Choose raft over ColossalAI when raft is primarily Cuda; ColossalAI is Python; 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 ColossalAI? You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems. Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series). You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7. ### 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 ColossalAI or raft more popular on GitHub? ColossalAI has more GitHub stars (41,432 vs 1,036). Stars measure visibility, not whether either tool fits your constraints. ### Are ColossalAI and raft open source? Yes - both are open-source projects on GitHub (ColossalAI: Apache-2.0, raft: Apache-2.0). ### Where can I find alternatives to ColossalAI or raft? GraphCanon lists graph-backed alternatives at [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) and [raft alternatives](/tools/nvidia-raft/alternatives) ([ColossalAI markdown twin](/tools/hpcaitech-colossalai/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/hpcaitech-colossalai-vs-nvidia-raft.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ColossalAI or raft? ColossalAI: 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 ColossalAI and raft? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ColossalAI trust report](/tools/hpcaitech-colossalai/trust); [raft trust report](/tools/nvidia-raft/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hpcaitech-colossalai`](/api/graphcanon/graph?tool=hpcaitech-colossalai) - 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/_