--- title: "FastChat vs mindspore" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lm-sys-fastchat-vs-mindspore-ai-mindspore" tools: ["lm-sys-fastchat", "mindspore-ai-mindspore"] --- # FastChat vs mindspore *GraphCanon updated Jul 12, 2026* ## Verdict 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; pick mindspore if mindSpore's core strengths lie in its flexibility across Ascend910, GPU CUDA 10.1, and CPU setups on multiple OSes; it excels in mobile. [FastChat](https://github.com/lm-sys/FastChat) reports 39k GitHub stars, 4.8k forks, and 1.0k open issues, last pushed May 1, 2026. [mindspore](https://gitee.com/mindspore/mindspore) has 4.7k stars, 752 forks, and 225 open issues, last pushed Jul 29, 2024. Figures are from public GitHub metadata via [FastChat's repository](https://github.com/lm-sys/FastChat) and [mindspore's repository](https://github.com/mindspore-ai/mindspore). | | [FastChat](/tools/lm-sys-fastchat.md) | [mindspore](/tools/mindspore-ai-mindspore.md) | | --- | --- | --- | | Tagline | An open platform for training, serving, and evaluating large language models | An open-source deep learning framework for mobile, edge and cloud scenarios. | | Stars | 39,490 | 4,694 | | Forks | 4,788 | 752 | | Open issues | 1,027 | 225 | | Language | Python | C++ | | 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 | MindSpore's core strengths lie in its flexibility across Ascend910, GPU CUDA 10.1, and CPU setups on multiple OSes; it excels in mobile, edge, and cloud scenarios. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FastChat](/tools/lm-sys-fastchat.md) | [mindspore](/tools/mindspore-ai-mindspore.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 71d | 712d | | Open issues (now) | 1.0k | 225 | | Security scan | No lockfile | 103 low (103 low) | | Full report | [trust report](/tools/lm-sys-fastchat/trust.md) | [trust report](/tools/mindspore-ai-mindspore/trust.md) | ## Shared compatibility - **Python**: [FastChat](/tools/lm-sys-fastchat.md) - Python runtime; [mindspore](/tools/mindspore-ai-mindspore.md) - Python runtime ## 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 ## Decision facts: mindspore - **Adopt for:** MindSpore's core strengths lie in its flexibility across Ascend910, GPU CUDA 10.1, and CPU setups on multiple OSes; it excels in mobile, edge, and cloud scenarios. ## Choose when ### Choose FastChat if… - FastChat is primarily Python; mindspore is C++. - Tags unique to FastChat: chatbots, distributed serving, evaluation system, large-language-models. - Also covers Evaluation & Observability, LLM Frameworks. - - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ### Choose mindspore if… - mindspore is primarily C++; FastChat is Python. - Tags unique to mindspore: ascend910, cpu support, deep-learning, gpu support. - When working with Huawei's Ascend hardware like Ascend910 ## 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. ## When NOT to use mindspore - Avoid if only NVIDIA GPUs without CUDA 10.1 support are available - Not ideal for users requiring non-LINUX (excluding Windows) environments beyond specified Ubuntu/CentOS/x86 versions - If development primarily targets hardware not covered by MindSpore's Ascend, CUDA, or CPU setups ## Common questions ### What is the difference between FastChat and mindspore? FastChat: An open platform for training, serving, and evaluating large language models. mindspore: An open-source deep learning framework for mobile, edge and cloud scenarios.. See the comparison table for live GitHub stats and shared categories. ### When should I choose FastChat over mindspore? Choose FastChat over mindspore when FastChat is primarily Python; mindspore is C++; Tags unique to FastChat: chatbots, distributed serving, evaluation system, large-language-models; Also covers Evaluation & Observability, LLM Frameworks; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ### When should I choose mindspore over FastChat? Choose mindspore over FastChat when mindspore is primarily C++; FastChat is Python; Tags unique to mindspore: ascend910, cpu support, deep-learning, gpu support; When working with Huawei's Ascend hardware like Ascend910. ### 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. ### When should I avoid mindspore? Avoid if only NVIDIA GPUs without CUDA 10.1 support are available Not ideal for users requiring non-LINUX (excluding Windows) environments beyond specified Ubuntu/CentOS/x86 versions If development primarily targets hardware not covered by MindSpore's Ascend, CUDA, or CPU setups ### Is FastChat or mindspore more popular on GitHub? FastChat has more GitHub stars (39,490 vs 4,694). Stars measure visibility, not whether either tool fits your constraints. ### Are FastChat and mindspore open source? Yes - both are open-source projects on GitHub (FastChat: Apache-2.0, mindspore: Apache-2.0). ### Where can I find alternatives to FastChat or mindspore? GraphCanon lists graph-backed alternatives at [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) and [mindspore alternatives](/tools/mindspore-ai-mindspore/alternatives) ([FastChat markdown twin](/tools/lm-sys-fastchat/alternatives.md), [mindspore markdown twin](/tools/mindspore-ai-mindspore/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/lm-sys-fastchat-vs-mindspore-ai-mindspore.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FastChat or mindspore? FastChat: Steady. mindspore: Dormant. 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 FastChat and mindspore? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FastChat trust report](/tools/lm-sys-fastchat/trust); [mindspore trust report](/tools/mindspore-ai-mindspore/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=lm-sys-fastchat`](/api/graphcanon/graph?tool=lm-sys-fastchat) - 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/_