--- title: "FastEdit vs aikit" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-fastedit-vs-kaito-project-aikit" tools: ["hiyouga-fastedit", "kaito-project-aikit"] --- # FastEdit vs aikit *GraphCanon updated Aug 24, 2026* ## Verdict Pick FastEdit if fastEdit is a Python library for quick edits to large language models using PyTorch; pick aikit if aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. [FastEdit](https://github.com/hiyouga/FastEdit) reports 1.4k GitHub stars, 103 forks, and 21 open issues, last pushed Aug 13, 2023. [aikit](https://kaito-project.github.io/aikit/) has 537 stars, 57 forks, and 40 open issues, last pushed Aug 24, 2026. Figures are from public GitHub metadata via [FastEdit's repository](https://github.com/hiyouga/FastEdit) and [aikit's repository](https://github.com/kaito-project/aikit). | | [FastEdit](/tools/hiyouga-fastedit.md) | [aikit](/tools/kaito-project-aikit.md) | | --- | --- | --- | | Tagline | Editing large language models within 10 seconds | Fine-tune, build, and deploy open-source LLMs easily! | | Stars | 1,370 | 537 | | Forks | 103 | 57 | | Open issues | 21 | 40 | | Language | Python | Go | | Adopt for | FastEdit is a Python library for quick edits to large language models using PyTorch. | Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | LLM Frameworks | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FastEdit](/tools/hiyouga-fastedit.md) | [aikit](/tools/kaito-project-aikit.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1086d | 0d | | Open issues (now) | 21 | 40 | | Stars delta | Unknown | +3 (30d) | | Open issues delta | Unknown | -3 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/hiyouga-fastedit/trust.md) | [trust report](/tools/kaito-project-aikit/trust.md) | ## Decision facts: FastEdit - **Requirements:** Min -1 GB RAM; Requires Python 3.8+ and PyTorch 1.13.1+. Must also install 🤗Transformers, Datasets, Accelerate, sentencepiece, and fire.; Hardware requirements for a specific model can vary; refer to the provided table for minimum RAM sizes for different models. - **Adopt for:** FastEdit is a Python library for quick edits to large language models using PyTorch. ## Decision facts: aikit - **Adopt for:** Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. ## Choose when ### Choose FastEdit if… - FastEdit is primarily Python; aikit is Go. - License: FastEdit is Apache-2.0, aikit is MIT. - Requirements: Min -1 GB RAM; Requires Python 3.8+ and PyTorch 1.13.1+. Must also install 🤗Transformers, Datasets, Accelerate, sentencepiece, and fire.; Hardware requirements for a specific model can vary; refer to the provided table for minimum RAM sizes for different models.. - Tags unique to FastEdit: bloom, chatbots, falcon, large language models. - When rapid iterations on language model edits are necessary, such as testing and tuning with tight feedback loops. ### Choose aikit if… - aikit is primarily Go; FastEdit is Python. - License: aikit is MIT, FastEdit is Apache-2.0. - Tags unique to aikit: ai, buildkit, docker, fine-tuning. - Also covers Inference & Serving, Model Training. - aikit ships Docker support for self-hosted deployment. - - You need a flexible solution specifically built using Go and prefer its concurrency model. ## When NOT to use FastEdit - If your workflow requires integration with TensorFlow instead of PyTorch, since FastEdit is built on top of PyTorch. - For hardware configurations that cannot meet the fast editing mode's requirements; for instance, if you have less than 24GB RAM available. - If rapid edits within seconds are not a priority and longer processing times can be tolerated. ## When NOT to use aikit - - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit. - - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these. ## Common questions ### What is the difference between FastEdit and aikit? FastEdit: Editing large language models within 10 seconds. aikit: Fine-tune, build, and deploy open-source LLMs easily!. See the comparison table for live GitHub stats and shared categories. ### When should I choose FastEdit over aikit? Choose FastEdit over aikit when FastEdit is primarily Python; aikit is Go; License: FastEdit is Apache-2.0, aikit is MIT; Requirements: Min -1 GB RAM; Requires Python 3.8+ and PyTorch 1.13.1+. Must also install 🤗Transformers, Datasets, Accelerate, sentencepiece, and fire.; Hardware requirements for a specific model can vary; refer to the provided table for minimum RAM sizes for different models.; Tags unique to FastEdit: bloom, chatbots, falcon, large language models; When rapid iterations on language model edits are necessary, such as testing and tuning with tight feedback loops. ### When should I choose aikit over FastEdit? Choose aikit over FastEdit when aikit is primarily Go; FastEdit is Python; License: aikit is MIT, FastEdit is Apache-2.0; Tags unique to aikit: ai, buildkit, docker, fine-tuning; Also covers Inference & Serving, Model Training; aikit ships Docker support for self-hosted deployment; - You need a flexible solution specifically built using Go and prefer its concurrency model. ### When should I avoid FastEdit? If your workflow requires integration with TensorFlow instead of PyTorch, since FastEdit is built on top of PyTorch. For hardware configurations that cannot meet the fast editing mode's requirements; for instance, if you have less than 24GB RAM available. If rapid edits within seconds are not a priority and longer processing times can be tolerated. ### When should I avoid aikit? - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit. - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these. ### Is FastEdit or aikit more popular on GitHub? FastEdit has more GitHub stars (1,370 vs 537). Stars measure visibility, not whether either tool fits your constraints. ### Are FastEdit and aikit open source? Yes - both are open-source projects on GitHub (FastEdit: Apache-2.0, aikit: MIT). ### Where can I find alternatives to FastEdit or aikit? GraphCanon lists graph-backed alternatives at [FastEdit alternatives](/tools/hiyouga-fastedit/alternatives) and [aikit alternatives](/tools/kaito-project-aikit/alternatives) ([FastEdit markdown twin](/tools/hiyouga-fastedit/alternatives.md), [aikit markdown twin](/tools/kaito-project-aikit/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/hiyouga-fastedit-vs-kaito-project-aikit.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FastEdit or aikit? FastEdit: Dormant. aikit: 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 FastEdit and aikit? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FastEdit trust report](/tools/hiyouga-fastedit/trust); [aikit trust report](/tools/kaito-project-aikit/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hiyouga-fastedit`](/api/graphcanon/graph?tool=hiyouga-fastedit) - 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/_