--- title: "FastEdit vs FastChat" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-fastedit-vs-lm-sys-fastchat" tools: ["hiyouga-fastedit", "lm-sys-fastchat"] --- # FastEdit vs FastChat *GraphCanon updated Aug 7, 2026* ## Verdict Pick FastEdit if fastEdit is a Python library for quick edits to large language models using PyTorch; pick FastChat if fastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models via web UIs and RESTful APIs. It powers ChatB. [FastEdit](https://github.com/hiyouga/FastEdit) reports 1.4k GitHub stars, 103 forks, and 21 open issues, last pushed Aug 13, 2023. [FastChat](https://github.com/lm-sys/FastChat) has 40k stars, 4.8k forks, and 1.0k open issues, last pushed May 1, 2026. Figures are from public GitHub metadata via [FastEdit's repository](https://github.com/hiyouga/FastEdit) and [FastChat's repository](https://github.com/lm-sys/FastChat). | | [FastEdit](/tools/hiyouga-fastedit.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Tagline | Editing large language models within 10 seconds | An open platform for training, serving, and evaluating large language models | | Stars | 1,370 | 39,517 | | Forks | 103 | 4,788 | | Open issues | 21 | 1,038 | | Language | Python | Python | | Adopt for | FastEdit is a Python library for quick edits to large language models using PyTorch. | FastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models via web UIs and RESTful APIs. It powers ChatB | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | LLM Frameworks | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FastEdit](/tools/hiyouga-fastedit.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1086d | 98d | | Open issues (now) | 21 | 1.0k | | Owner type | User | Organization | | Full report | [trust report](/tools/hiyouga-fastedit/trust.md) | [trust report](/tools/lm-sys-fastchat/trust.md) | ## Shared compatibility - **Python**: [FastEdit](/tools/hiyouga-fastedit.md) - Python runtime; [FastChat](/tools/lm-sys-fastchat.md) - Python runtime ## 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: FastChat - **Adopt for:** FastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models via web UIs and RESTful APIs. It powers ChatB ## Choose when ### Choose FastEdit if… - 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, chatgpt, falcon, gpt. - When rapid iterations on language model edits are necessary, such as testing and tuning with tight feedback loops. ### Choose FastChat if… - Tags unique to FastChat: distributed-serving, evaluation system. - Also covers Evaluation & Observability, Inference & Serving, Model Training. - - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ## 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 FastChat - - You require a proprietary or closed-source framework; FastChat is open-source under Apache-2.0 license and its use might be unsuitable for environments requiring proprietary solutions. - - Your chatbot evaluation needs do not align with the types of data used in FastChat's datasets (e.g., human votes, MT-Bench evaluations). - - You prefer a more user-friendly setup without the need to clone a repository and manually install dependencies; FastChat requires installation from source with additional steps for Rust and CMake on - + Mac. ## Common questions ### What is the difference between FastEdit and FastChat? FastEdit: Editing large language models within 10 seconds. FastChat: An open platform for training, serving, and evaluating large language models. See the comparison table for live GitHub stats and shared categories. ### When should I choose FastEdit over FastChat? Choose FastEdit over FastChat when 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, chatgpt, falcon, gpt; When rapid iterations on language model edits are necessary, such as testing and tuning with tight feedback loops. ### When should I choose FastChat over FastEdit? Choose FastChat over FastEdit when Tags unique to FastChat: distributed-serving, evaluation system; Also covers Evaluation & Observability, Inference & Serving, Model Training; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ### 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 FastChat? - You require a proprietary or closed-source framework; FastChat is open-source under Apache-2.0 license and its use might be unsuitable for environments requiring proprietary solutions. - Your chatbot evaluation needs do not align with the types of data used in FastChat's datasets (e.g., human votes, MT-Bench evaluations). - You prefer a more user-friendly setup without the need to clone a repository and manually install dependencies; FastChat requires installation from source with additional steps for Rust and CMake on + Mac. ### Is FastEdit or FastChat more popular on GitHub? FastChat has more GitHub stars (39,517 vs 1,370). Stars measure visibility, not whether either tool fits your constraints. ### Are FastEdit and FastChat open source? Yes - both are open-source projects on GitHub (FastEdit: Apache-2.0, FastChat: Apache-2.0). ### Where can I find alternatives to FastEdit or FastChat? GraphCanon lists graph-backed alternatives at [FastEdit alternatives](/tools/hiyouga-fastedit/alternatives) and [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) ([FastEdit markdown twin](/tools/hiyouga-fastedit/alternatives.md), [FastChat markdown twin](/tools/lm-sys-fastchat/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-lm-sys-fastchat.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FastEdit or FastChat? FastEdit: Dormant. FastChat: 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 FastEdit and FastChat? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FastEdit trust report](/tools/hiyouga-fastedit/trust); [FastChat trust report](/tools/lm-sys-fastchat/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/_