--- title: "FastChat vs server" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lm-sys-fastchat-vs-triton-inference-server-server" tools: ["lm-sys-fastchat", "triton-inference-server-server"] --- # FastChat vs server *GraphCanon updated Jul 12, 2026* ## Verdict Pick FastChat when license: FastChat is Apache-2.0, server is BSD-3-Clause; pick server when license: server is BSD-3-Clause, FastChat is Apache-2.0. [FastChat](https://github.com/lm-sys/FastChat) reports 39k GitHub stars, 4.8k forks, and 1.0k open issues, last pushed May 1, 2026. [server](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html) has 11k stars, 1.8k forks, and 901 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [FastChat's repository](https://github.com/lm-sys/FastChat) and [server's repository](https://github.com/triton-inference-server/server). | | [FastChat](/tools/lm-sys-fastchat.md) | [server](/tools/triton-inference-server-server.md) | | --- | --- | --- | | Tagline | An open platform for training, serving, and evaluating large language models | The Triton Inference Server provides an optimized cloud and edge inferencing solution. | | Stars | 39,490 | 10,822 | | Forks | 4,788 | 1,806 | | Open issues | 1,027 | 901 | | Language | Python | 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 | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | BSD-3-Clause | | Categories | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Inference & Serving, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FastChat](/tools/lm-sys-fastchat.md) | [server](/tools/triton-inference-server-server.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 71d | 0d | | Open issues (now) | 1.0k | 901 | | Full report | [trust report](/tools/lm-sys-fastchat/trust.md) | [trust report](/tools/triton-inference-server-server/trust.md) | ## Shared compatibility - **Python**: [FastChat](/tools/lm-sys-fastchat.md) - Python runtime; [server](/tools/triton-inference-server-server.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 ## Choose when ### Choose FastChat if… - License: FastChat is Apache-2.0, server is BSD-3-Clause. - 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 server if… - License: server is BSD-3-Clause, FastChat is Apache-2.0. - Tags unique to server: cloud, datacenter, deep-learning, edge. - Also covers Speech & Audio. ## 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 server - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between FastChat and server? FastChat: An open platform for training, serving, and evaluating large language models. server: The Triton Inference Server provides an optimized cloud and edge inferencing solution.. See the comparison table for live GitHub stats and shared categories. ### When should I choose FastChat over server? Choose FastChat over server when License: FastChat is Apache-2.0, server is BSD-3-Clause; 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 server over FastChat? Choose server over FastChat when License: server is BSD-3-Clause, FastChat is Apache-2.0; Tags unique to server: cloud, datacenter, deep-learning, edge; Also covers Speech & Audio. ### 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 server? Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is FastChat or server more popular on GitHub? FastChat has more GitHub stars (39,490 vs 10,822). Stars measure visibility, not whether either tool fits your constraints. ### Are FastChat and server open source? Yes - both are open-source projects on GitHub (FastChat: Apache-2.0, server: BSD-3-Clause). ### Where can I find alternatives to FastChat or server? GraphCanon lists graph-backed alternatives at [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) and [server alternatives](/tools/triton-inference-server-server/alternatives) ([FastChat markdown twin](/tools/lm-sys-fastchat/alternatives.md), [server markdown twin](/tools/triton-inference-server-server/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-triton-inference-server-server.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FastChat or server? FastChat: Steady. server: Very active. 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 server? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FastChat trust report](/tools/lm-sys-fastchat/trust); [server trust report](/tools/triton-inference-server-server/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/_