--- title: "FastChat vs LLMs-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lm-sys-fastchat-vs-rasbt-llms-from-scratch" tools: ["lm-sys-fastchat", "rasbt-llms-from-scratch"] --- # FastChat vs LLMs-from-scratch *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 LLMs-from-scratch if lLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions. [FastChat](https://github.com/lm-sys/FastChat) reports 39k GitHub stars, 4.8k forks, and 1.0k open issues, last pushed May 1, 2026. [LLMs-from-scratch](https://amzn.to/4fqvn0D) has 99k stars, 15k forks, and 4 open issues, last pushed Jun 2, 2026. Figures are from public GitHub metadata via [FastChat's repository](https://github.com/lm-sys/FastChat) and [LLMs-from-scratch's repository](https://github.com/rasbt/LLMs-from-scratch). | | [FastChat](/tools/lm-sys-fastchat.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Tagline | An open platform for training, serving, and evaluating large language models | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step | | Stars | 39,490 | 98,899 | | Forks | 4,788 | 15,183 | | Open issues | 1,027 | 4 | | Language | Python | Jupyter Notebook | | 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 | LLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | Model Training, LLM Frameworks, Inference & Serving, Evaluation & Observability | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FastChat](/tools/lm-sys-fastchat.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Days since push | 71d | 38d | | Open issues (now) | 1.0k | 4 | | Owner type | Organization | User | | Full report | [trust report](/tools/lm-sys-fastchat/trust.md) | [trust report](/tools/rasbt-llms-from-scratch/trust.md) | ## 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: LLMs-from-scratch - **Adopt for:** LLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions. ## Choose when ### Choose FastChat if… - FastChat is primarily Python; LLMs-from-scratch is Jupyter Notebook. - License: FastChat is Apache-2.0, LLMs-from-scratch is Other. - Tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving. - Also covers Inference & Serving, Evaluation & Observability. - - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ### Choose LLMs-from-scratch if… - LLMs-from-scratch is primarily Jupyter Notebook; FastChat is Python. - License: LLMs-from-scratch is Other, FastChat is Apache-2.0. - Tags unique to LLMs-from-scratch: deep-learning, ai, artificial-intelligence, attention-mechanism. - - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ## 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 LLMs-from-scratch - - If you are looking for a rapid deployment of an LLM without understanding its intricate structure - this tool requires extensive manual and conceptual work. - - You prefer frameworks with automatic model generation or other high-level abstractions that simplify the process. This repository emphasizes manual creation, which is more time-consuming but offers丰 - a deeper learning experience. ## Common questions ### What is the difference between FastChat and LLMs-from-scratch? FastChat: An open platform for training, serving, and evaluating large language models. LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step. See the comparison table for live GitHub stats and shared categories. ### When should I choose FastChat over LLMs-from-scratch? Choose FastChat over LLMs-from-scratch when FastChat is primarily Python; LLMs-from-scratch is Jupyter Notebook; License: FastChat is Apache-2.0, LLMs-from-scratch is Other; Tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving; Also covers Inference & Serving, Evaluation & Observability; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ### When should I choose LLMs-from-scratch over FastChat? Choose LLMs-from-scratch over FastChat when LLMs-from-scratch is primarily Jupyter Notebook; FastChat is Python; License: LLMs-from-scratch is Other, FastChat is Apache-2.0; Tags unique to LLMs-from-scratch: deep-learning, ai, artificial-intelligence, attention-mechanism; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ### 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 LLMs-from-scratch? - If you are looking for a rapid deployment of an LLM without understanding its intricate structure - this tool requires extensive manual and conceptual work. - You prefer frameworks with automatic model generation or other high-level abstractions that simplify the process. This repository emphasizes manual creation, which is more time-consuming but offers丰 a deeper learning experience. ### Is FastChat or LLMs-from-scratch more popular on GitHub? LLMs-from-scratch has more GitHub stars (98,899 vs 39,490). Stars measure visibility, not whether either tool fits your constraints. ### Are FastChat and LLMs-from-scratch open source? Yes - both are open-source projects on GitHub (FastChat: Apache-2.0, LLMs-from-scratch: Other). ### Where can I find alternatives to FastChat or LLMs-from-scratch? GraphCanon lists graph-backed alternatives at [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) and [LLMs-from-scratch alternatives](/tools/rasbt-llms-from-scratch/alternatives) ([FastChat markdown twin](/tools/lm-sys-fastchat/alternatives.md), [LLMs-from-scratch markdown twin](/tools/rasbt-llms-from-scratch/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-rasbt-llms-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FastChat or LLMs-from-scratch? FastChat: Steady. LLMs-from-scratch: Steady. 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 LLMs-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FastChat trust report](/tools/lm-sys-fastchat/trust); [LLMs-from-scratch trust report](/tools/rasbt-llms-from-scratch/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/_