--- title: "ColossalAI vs verl" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hpcaitech-colossalai-vs-verl-project-verl" tools: ["hpcaitech-colossalai", "verl-project-verl"] --- # ColossalAI vs verl *GraphCanon updated Jul 11, 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 verl if verl/HybridFlow is a specialized Python framework for post-training reinforcement learning (RL) that provides detailed documentation and reproducible baselines. It supports PPO and GRPO algorithms and includes Ray Trains. [ColossalAI](https://www.colossalai.org) reports 41k GitHub stars, 4.5k forks, and 501 open issues, last pushed May 25, 2026. [verl](https://verl.readthedocs.io/en/latest/index.html) has 22k stars, 4.2k forks, and 1.6k open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI) and [verl's repository](https://github.com/verl-project/verl). | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [verl](/tools/verl-project-verl.md) | | --- | --- | --- | | Tagline | Making large AI models cheaper, faster and more accessible | A Flexible and Efficient RL Post-Training Framework | | Stars | 41,408 | 22,425 | | Forks | 4,504 | 4,201 | | Open issues | 501 | 1,576 | | Language | Python | Python | | Adopt for | ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models. | verl/HybridFlow is a specialized Python framework for post-training reinforcement learning (RL) that provides detailed documentation and reproducible baselines. It supports PPO and GRPO algorithms and includes Ray Trains | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Inference & Serving, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [verl](/tools/verl-project-verl.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 46d | 0d | | Open issues (now) | 501 | 1.6k | | Security scan | No lockfile | 2 low (2 low) | | Full report | [trust report](/tools/hpcaitech-colossalai/trust.md) | [trust report](/tools/verl-project-verl/trust.md) | ## Shared compatibility - **Python**: [ColossalAI](/tools/hpcaitech-colossalai.md) - Python runtime; [verl](/tools/verl-project-verl.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: verl - **Pricing:** freemium - verl operates under the Apache-2.0 license and is free and open-source. However, you might incur costs associated with cloud services like AWS SageMaker if you plan to deploy large-scale projects on a - **Requirements:** Min 8 GB RAM; Ensure your development environment supports Python and the backend systems you intend to use (FSDP or Megatron-LM). - **Adopt for:** verl/HybridFlow is a specialized Python framework for post-training reinforcement learning (RL) that provides detailed documentation and reproducible baselines. It supports PPO and GRPO algorithms and includes Ray Trains ## Choose when ### Choose ColossalAI if… - 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 verl if… - Pricing: verl operates under the Apache-2.0 license and is free and open-source. However, you might incur costs associated with cloud services like AWS SageMaker if you plan to deploy large-scale projects on a. - Requirements: Min 8 GB RAM; Ensure your development environment supports Python and the backend systems you intend to use (FSDP or Megatron-LM).. - Tags unique to verl: grpo, post-training, ppo, python. - Opt for verl if your project requires flexibility in integrating advanced backend systems like FSDP or Megatron-LM to extend RL model capabilities. ## 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 verl - Avoid verl if your project does not require advanced backend integration with systems like FSDP or Megatron-LM; it might be overkill and introduce unnecessary complexity. - Do not use if detailed documentation is less important to your workflow. While verl excels in this area, simpler frameworks may suffice for lighter requirements. ## Common questions ### What is the difference between ColossalAI and verl? ColossalAI: Making large AI models cheaper, faster and more accessible. verl: A Flexible and Efficient RL Post-Training Framework. See the comparison table for live GitHub stats and shared categories. ### When should I choose ColossalAI over verl? Choose ColossalAI over verl when 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 verl over ColossalAI? Choose verl over ColossalAI when Pricing: verl operates under the Apache-2.0 license and is free and open-source. However, you might incur costs associated with cloud services like AWS SageMaker if you plan to deploy large-scale projects on a; Requirements: Min 8 GB RAM; Ensure your development environment supports Python and the backend systems you intend to use (FSDP or Megatron-LM).; Tags unique to verl: grpo, post-training, ppo, python; Opt for verl if your project requires flexibility in integrating advanced backend systems like FSDP or Megatron-LM to extend RL model capabilities. ### 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 verl? Avoid verl if your project does not require advanced backend integration with systems like FSDP or Megatron-LM; it might be overkill and introduce unnecessary complexity. Do not use if detailed documentation is less important to your workflow. While verl excels in this area, simpler frameworks may suffice for lighter requirements. ### Is ColossalAI or verl more popular on GitHub? ColossalAI has more GitHub stars (41,408 vs 22,425). Stars measure visibility, not whether either tool fits your constraints. ### Are ColossalAI and verl open source? Yes - both are open-source projects on GitHub (ColossalAI: Apache-2.0, verl: Apache-2.0). ### Where can I find alternatives to ColossalAI or verl? GraphCanon lists graph-backed alternatives at [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) and [verl alternatives](/tools/verl-project-verl/alternatives) ([ColossalAI markdown twin](/tools/hpcaitech-colossalai/alternatives.md), [verl markdown twin](/tools/verl-project-verl/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-verl-project-verl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ColossalAI or verl? ColossalAI: Steady. verl: 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 verl? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ColossalAI trust report](/tools/hpcaitech-colossalai/trust); [verl trust report](/tools/verl-project-verl/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/_