--- title: "DeepSpeed vs FastChat" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepspeedai-deepspeed-vs-lm-sys-fastchat" tools: ["deepspeedai-deepspeed", "lm-sys-fastchat"] --- # DeepSpeed vs FastChat *GraphCanon updated Jul 11, 2026* ## Verdict Pick DeepSpeed if decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression; 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. [DeepSpeed](https://www.deepspeed.ai/) reports 43k GitHub stars, 4.9k forks, and 1.3k open issues, last pushed Jul 11, 2026. [FastChat](https://github.com/lm-sys/FastChat) has 39k stars, 4.8k forks, and 1.0k open issues, last pushed May 1, 2026. Figures are from public GitHub metadata via [DeepSpeed's repository](https://github.com/deepspeedai/DeepSpeed) and [FastChat's repository](https://github.com/lm-sys/FastChat). | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Tagline | Deep learning optimization library for efficient distributed training and inference | An open platform for training, serving, and evaluating large language models | | Stars | 42,685 | 39,490 | | Forks | 4,883 | 4,788 | | Open issues | 1,302 | 1,027 | | Language | Python | Python | | Adopt for | Decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression. | 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 | 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._ | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 71d | | Open issues (now) | 1.3k | 1.0k | | Full report | [trust report](/tools/deepspeedai-deepspeed/trust.md) | [trust report](/tools/lm-sys-fastchat/trust.md) | ## Decision facts: DeepSpeed - **Adopt for:** Decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression. ## 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 DeepSpeed if… - Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, deep-learning. - - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters) - More GitHub stars (43k vs 39k) - visibility, not fit. ### Choose FastChat if… - 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 NOT to use DeepSpeed - - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs - - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively ## 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. ## Common questions ### What is the difference between DeepSpeed and FastChat? DeepSpeed: Deep learning optimization library for efficient distributed training and inference. 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 DeepSpeed over FastChat? Choose DeepSpeed over FastChat when Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, deep-learning; - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters); More GitHub stars (43k vs 39k) - visibility, not fit. ### When should I choose FastChat over DeepSpeed? Choose FastChat over DeepSpeed when 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 avoid DeepSpeed? - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively ### 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. ### Is DeepSpeed or FastChat more popular on GitHub? DeepSpeed has more GitHub stars (42,685 vs 39,490). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSpeed and FastChat open source? Yes - both are open-source projects on GitHub (DeepSpeed: Apache-2.0, FastChat: Apache-2.0). ### Where can I find alternatives to DeepSpeed or FastChat? GraphCanon lists graph-backed alternatives at [DeepSpeed alternatives](/tools/deepspeedai-deepspeed/alternatives) and [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) ([DeepSpeed markdown twin](/tools/deepspeedai-deepspeed/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/deepspeedai-deepspeed-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, DeepSpeed or FastChat? DeepSpeed: Very active. FastChat: 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 DeepSpeed and FastChat? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSpeed trust report](/tools/deepspeedai-deepspeed/trust); [FastChat trust report](/tools/lm-sys-fastchat/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=deepspeedai-deepspeed`](/api/graphcanon/graph?tool=deepspeedai-deepspeed) - 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/_