--- title: "ColossalAI vs contextualized-topic-models" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hpcaitech-colossalai-vs-milanlproc-contextualized-topic-models" tools: ["hpcaitech-colossalai", "milanlproc-contextualized-topic-models"] --- # ColossalAI vs contextualized-topic-models *GraphCanon updated Jul 12, 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 contextualized-topic-models if contextualized-topic-models is a Python package that enhances traditional topic modeling by integrating contextualized embeddings like BERT. [ColossalAI](https://www.colossalai.org) reports 41k GitHub stars, 4.5k forks, and 501 open issues, last pushed May 25, 2026. [contextualized-topic-models](https://github.com/MilaNLProc/contextualized-topic-models) has 1.3k stars, 154 forks, and 11 open issues, last pushed Jul 24, 2025. Figures are from public GitHub metadata via [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI) and [contextualized-topic-models's repository](https://github.com/MilaNLProc/contextualized-topic-models). | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [contextualized-topic-models](/tools/milanlproc-contextualized-topic-models.md) | | --- | --- | --- | | Tagline | Making large AI models cheaper, faster and more accessible | A python package for contextualized topic modeling using BERT and other embeddings. | | Stars | 41,408 | 1,272 | | Forks | 4,504 | 154 | | Open issues | 501 | 11 | | 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. | Contextualized-topic-models is a Python package that enhances traditional topic modeling by integrating contextualized embeddings like BERT. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Inference & Serving, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [contextualized-topic-models](/tools/milanlproc-contextualized-topic-models.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 46d | 352d | | Open issues (now) | 501 | 11 | | Full report | [trust report](/tools/hpcaitech-colossalai/trust.md) | [trust report](/tools/milanlproc-contextualized-topic-models/trust.md) | ## 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: contextualized-topic-models - **Adopt for:** Contextualized-topic-models is a Python package that enhances traditional topic modeling by integrating contextualized embeddings like BERT. ## Choose when ### Choose ColossalAI if… - License: ColossalAI is Apache-2.0, contextualized-topic-models is MIT. - 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 contextualized-topic-models if… - License: contextualized-topic-models is MIT, ColossalAI is Apache-2.0. - Tags unique to contextualized-topic-models: bert, embeddings, multilingual-models, neural-topic-models. - - When you need to analyze text data with enriched topic coherence provided by models utilizing BERT-like embeddings. ## 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 contextualized-topic-models - - If your project does not require advanced contextual embedding integration and more conventional topic modeling techniques suffice. - - In scenarios where model complexity can be a bottleneck for real-time processing or when working with hardware limitations that cannot efficiently process BERT embeddings. ## Common questions ### What is the difference between ColossalAI and contextualized-topic-models? ColossalAI: Making large AI models cheaper, faster and more accessible. contextualized-topic-models: A python package for contextualized topic modeling using BERT and other embeddings.. See the comparison table for live GitHub stats and shared categories. ### When should I choose ColossalAI over contextualized-topic-models? Choose ColossalAI over contextualized-topic-models when License: ColossalAI is Apache-2.0, contextualized-topic-models is MIT; 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 contextualized-topic-models over ColossalAI? Choose contextualized-topic-models over ColossalAI when License: contextualized-topic-models is MIT, ColossalAI is Apache-2.0; Tags unique to contextualized-topic-models: bert, embeddings, multilingual-models, neural-topic-models; - When you need to analyze text data with enriched topic coherence provided by models utilizing BERT-like embeddings. ### 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 contextualized-topic-models? - If your project does not require advanced contextual embedding integration and more conventional topic modeling techniques suffice. - In scenarios where model complexity can be a bottleneck for real-time processing or when working with hardware limitations that cannot efficiently process BERT embeddings. ### Is ColossalAI or contextualized-topic-models more popular on GitHub? ColossalAI has more GitHub stars (41,408 vs 1,272). Stars measure visibility, not whether either tool fits your constraints. ### Are ColossalAI and contextualized-topic-models open source? Yes - both are open-source projects on GitHub (ColossalAI: Apache-2.0, contextualized-topic-models: MIT). ### Where can I find alternatives to ColossalAI or contextualized-topic-models? GraphCanon lists graph-backed alternatives at [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) and [contextualized-topic-models alternatives](/tools/milanlproc-contextualized-topic-models/alternatives) ([ColossalAI markdown twin](/tools/hpcaitech-colossalai/alternatives.md), [contextualized-topic-models markdown twin](/tools/milanlproc-contextualized-topic-models/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-milanlproc-contextualized-topic-models.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ColossalAI or contextualized-topic-models? ColossalAI: Steady. contextualized-topic-models: Slowing. 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 contextualized-topic-models? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ColossalAI trust report](/tools/hpcaitech-colossalai/trust); [contextualized-topic-models trust report](/tools/milanlproc-contextualized-topic-models/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/_