--- title: "FastChat vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lm-sys-fastchat-vs-mlabonne-llm-course" tools: ["lm-sys-fastchat", "mlabonne-llm-course"] --- # FastChat vs llm-course *GraphCanon updated Jul 11, 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 llm-course if the llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and. [FastChat](https://github.com/lm-sys/FastChat) reports 39k GitHub stars, 4.8k forks, and 1.0k open issues, last pushed May 1, 2026. [llm-course](https://mlabonne.github.io/blog/) has 81k stars, 9.4k forks, and 84 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [FastChat's repository](https://github.com/lm-sys/FastChat) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [FastChat](/tools/lm-sys-fastchat.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | An open platform for training, serving, and evaluating large language models | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 39,490 | 80,839 | | Forks | 4,788 | 9,421 | | Open issues | 1,027 | 84 | | Language | Python | - | | 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 | The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FastChat](/tools/lm-sys-fastchat.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 71d | 155d | | Open issues (now) | 1.0k | 84 | | Owner type | Organization | User | | Full report | [trust report](/tools/lm-sys-fastchat/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) | ## Shared compatibility - **Python**: [FastChat](/tools/lm-sys-fastchat.md) - Python runtime; [llm-course](/tools/mlabonne-llm-course.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: llm-course - **Requirements:** Course materials are available in Colab notebooks; access requires a Google account - **Adopt for:** The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to - **License detail:** Apache-2.0 ## Choose when ### Choose FastChat if… - Tags unique to FastChat: chatbots, distributed serving, evaluation system. - - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. - More recently updated (last pushed May 1, 2026). ### Choose llm-course if… - Requirements: Course materials are available in Colab notebooks; access requires a Google account. - Tags unique to llm-course: colab-notebooks, course, machine-learning, roadmap. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ## 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 llm-course - - If you only require a quick introduction to LLMs without deep dive into core components - - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ## Common questions ### What is the difference between FastChat and llm-course? FastChat: An open platform for training, serving, and evaluating large language models. llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. See the comparison table for live GitHub stats and shared categories. ### When should I choose FastChat over llm-course? Choose FastChat over llm-course when Tags unique to FastChat: chatbots, distributed serving, evaluation system; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench; More recently updated (last pushed May 1, 2026). ### When should I choose llm-course over FastChat? Choose llm-course over FastChat when Requirements: Course materials are available in Colab notebooks; access requires a Google account; Tags unique to llm-course: colab-notebooks, course, machine-learning, roadmap; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### 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 llm-course? - If you only require a quick introduction to LLMs without deep dive into core components - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ### Is FastChat or llm-course more popular on GitHub? llm-course has more GitHub stars (80,839 vs 39,490). Stars measure visibility, not whether either tool fits your constraints. ### Are FastChat and llm-course open source? Yes - both are open-source projects on GitHub (FastChat: Apache-2.0, llm-course: Apache-2.0). ### Where can I find alternatives to FastChat or llm-course? GraphCanon lists graph-backed alternatives at [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([FastChat markdown twin](/tools/lm-sys-fastchat/alternatives.md), [llm-course markdown twin](/tools/mlabonne-llm-course/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-mlabonne-llm-course.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FastChat or llm-course? FastChat: Steady. llm-course: 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 FastChat and llm-course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FastChat trust report](/tools/lm-sys-fastchat/trust); [llm-course trust report](/tools/mlabonne-llm-course/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/_