--- title: "handy-ollama vs FastChat" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-handy-ollama-vs-lm-sys-fastchat" tools: ["datawhalechina-handy-ollama", "lm-sys-fastchat"] --- # handy-ollama vs FastChat *GraphCanon updated Sep 20, 2026* ## Verdict Pick handy-ollama if handy-ollama is a guide for deploying large language models using Ollama on CPU systems via Jupyter Notebooks; pick FastChat if fastChat is an open-source platform for training, serving, and evaluating large language models, including Vicuna, and supports Chatbot Arena, a platform for LLM evaluations with human feedback. [handy-ollama](https://datawhalechina.github.io/handy-ollama/) reports 2.5k GitHub stars, 321 forks, and 8 open issues, last pushed Jan 15, 2026. [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 [handy-ollama's repository](https://github.com/datawhalechina/handy-ollama) and [FastChat's repository](https://github.com/lm-sys/FastChat). | | [handy-ollama](/tools/datawhalechina-handy-ollama.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Tagline | Hands-On Ollama with CPU for Large Model Deployment | An open platform for training, serving, and evaluating large language models | | Stars | 2,532 | 39,536 | | Forks | 321 | 4,774 | | Open issues | 8 | 1,043 | | Language | Jupyter Notebook | Python | | Adopt for | handy-ollama is a guide for deploying large language models using Ollama on CPU systems via Jupyter Notebooks. | FastChat is an open-source platform for training, serving, and evaluating large language models, including Vicuna, and supports Chatbot Arena, a platform for LLM evaluations with human feedback. | | Persona | - | - | | Runtime | - | - | | License | handy-ollama is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). | Apache-2.0 | | Categories | Inference & Serving, Model Training | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [handy-ollama](/tools/datawhalechina-handy-ollama.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Days since push | 247d | 140d | | Open issues (now) | 8 | 1.0k | | Stars delta | +33 (30d) | +19 (30d) | | Open issues delta | 0 (30d) | +5 (30d) | | Full report | [trust report](/tools/datawhalechina-handy-ollama/trust.md) | [trust report](/tools/lm-sys-fastchat/trust.md) | ## Decision facts: handy-ollama - **Requirements:** Requires Ollama library for operations. - **Adopt for:** handy-ollama is a guide for deploying large language models using Ollama on CPU systems via Jupyter Notebooks. - **License detail:** handy-ollama is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). ## Decision facts: FastChat - **Adopt for:** FastChat is an open-source platform for training, serving, and evaluating large language models, including Vicuna, and supports Chatbot Arena, a platform for LLM evaluations with human feedback. ## Choose when ### Choose handy-ollama if… - handy-ollama is primarily Jupyter Notebook; FastChat is Python. - License: handy-ollama is Other, FastChat is Apache-2.0. - Requirements: Requires Ollama library for operations.. - Tags unique to handy-ollama: agent, gguf, langchain, large-language-models. - Use handy-ollama when you require specific guidance on deploying large models with the Ollama library exclusively on CPUs, as opposed to GPU-based alternatives. ### Choose FastChat if… - FastChat is primarily Python; handy-ollama is Jupyter Notebook. - License: FastChat is Apache-2.0, handy-ollama is Other. - Tags unique to FastChat: chatbot arena, llm-evaluation, multi-model serving, openai-compatible apis. - Also covers Evaluation & Observability, LLM Frameworks. - When you need a platform that supports both training and evaluation of large language models, such as Vicuna, and you want to leverage human feedback for model evaluation. ## When NOT to use handy-ollama - Avoid handy-ollama if you need support for deploying models on GPU or other hardware that is not specifically CPUs. - Do not use this guide if comprehensive tutorials in languages other than English are necessary, as the content appears to be primarily in Chinese and English. ## When NOT to use FastChat - If you are looking for a platform that focuses solely on model training without serving or evaluation capabilities, FastChat offers a comprehensive suite that might be more than you need. - If your project does not require human feedback for model evaluation, FastChat's integration with Chatbot Arena might be an unnecessary feature. - If you are working with proprietary or closed-source models, FastChat's open-source nature and reliance on open-source models like Vicuna might not align with your project requirements. ## Common questions ### What is the difference between handy-ollama and FastChat? handy-ollama: Hands-On Ollama with CPU for Large Model Deployment. 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 handy-ollama over FastChat? Choose handy-ollama over FastChat when handy-ollama is primarily Jupyter Notebook; FastChat is Python; License: handy-ollama is Other, FastChat is Apache-2.0; Requirements: Requires Ollama library for operations.; Tags unique to handy-ollama: agent, gguf, langchain, large-language-models; Use handy-ollama when you require specific guidance on deploying large models with the Ollama library exclusively on CPUs, as opposed to GPU-based alternatives. ### When should I choose FastChat over handy-ollama? Choose FastChat over handy-ollama when FastChat is primarily Python; handy-ollama is Jupyter Notebook; License: FastChat is Apache-2.0, handy-ollama is Other; Tags unique to FastChat: chatbot arena, llm-evaluation, multi-model serving, openai-compatible apis; Also covers Evaluation & Observability, LLM Frameworks; When you need a platform that supports both training and evaluation of large language models, such as Vicuna, and you want to leverage human feedback for model evaluation. ### When should I avoid handy-ollama? Avoid handy-ollama if you need support for deploying models on GPU or other hardware that is not specifically CPUs. Do not use this guide if comprehensive tutorials in languages other than English are necessary, as the content appears to be primarily in Chinese and English. ### When should I avoid FastChat? If you are looking for a platform that focuses solely on model training without serving or evaluation capabilities, FastChat offers a comprehensive suite that might be more than you need. If your project does not require human feedback for model evaluation, FastChat's integration with Chatbot Arena might be an unnecessary feature. If you are working with proprietary or closed-source models, FastChat's open-source nature and reliance on open-source models like Vicuna might not align with your project requirements. ### Is handy-ollama or FastChat more popular on GitHub? FastChat has more GitHub stars (39,536 vs 2,532). Stars measure visibility, not whether either tool fits your constraints. ### Are handy-ollama and FastChat open source? Yes - both are open-source projects on GitHub (handy-ollama: Other, FastChat: Apache-2.0). ### Where can I find alternatives to handy-ollama or FastChat? GraphCanon lists graph-backed alternatives at [handy-ollama alternatives](/tools/datawhalechina-handy-ollama/alternatives) and [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) ([handy-ollama markdown twin](/tools/datawhalechina-handy-ollama/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/datawhalechina-handy-ollama-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, handy-ollama or FastChat? handy-ollama: Slowing. 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 handy-ollama and FastChat? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [handy-ollama trust report](/tools/datawhalechina-handy-ollama/trust); [FastChat trust report](/tools/lm-sys-fastchat/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=datawhalechina-handy-ollama`](/api/graphcanon/graph?tool=datawhalechina-handy-ollama) - 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/_