Comparison
DeepSpeed vs FastChat
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.
Markdown twin · DeepSpeed alternatives · FastChat alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | DeepSpeed | FastChat |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Steady (71d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- DeepSpeed
- Deep learning optimization library for efficient distributed training and inference
- FastChat
- An open platform for training, serving, and evaluating large language models
Stars
- DeepSpeed
- 43k
- FastChat
- 39k
Forks
- DeepSpeed
- 4.9k
- FastChat
- 4.8k
Open issues
- DeepSpeed
- 1.3k
- FastChat
- 1.0k
Language
- DeepSpeed
- Python
- FastChat
- Python
Adopt for
- DeepSpeed
- 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
- 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
- DeepSpeed
- -
- FastChat
- -
Runtime
- DeepSpeed
- -
- FastChat
- -
License
- DeepSpeed
- Apache-2.0
- FastChat
- Apache-2.0
Last pushed
- DeepSpeed
- Jul 11, 2026
- FastChat
- May 1, 2026
Categories
- DeepSpeed
- Model Training, Inference & Serving
- FastChat
- LLM Frameworks, Model Training, Inference & Serving, Evaluation & Observability
Trust and health
Maintenance
- DeepSpeed
- Very active (96%)
- FastChat
- Steady (60%)
Days since push
- DeepSpeed
- 0d
- FastChat
- 71d
Open issues (now)
- DeepSpeed
- 1.3k
- FastChat
- 1.0k
Full report
- DeepSpeed
- Trust report
- FastChat
- Trust report
Choose DeepSpeed if…
- Tags unique to DeepSpeed: deep-learning, gpu, compression, machine-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 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
Choose FastChat if…
- Tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving.
- Also covers LLM Frameworks, Evaluation & Observability.
- - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench.
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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (deepspeedai/DeepSpeed) · observed Jul 11, 2026
- GitHub forks (deepspeedai/DeepSpeed) · observed Jul 11, 2026
- Last push (deepspeedai/DeepSpeed) · observed Jul 11, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (lm-sys/FastChat) · observed Jul 11, 2026
- GitHub forks (lm-sys/FastChat) · observed Jul 11, 2026
- Last push (lm-sys/FastChat) · observed May 1, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: DeepSpeed 43k · FastChat 39k (synced Jul 11, 2026).
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: deep-learning, gpu, compression, machine-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: evaluation system, large-language-models, chatbots, distributed serving; Also covers LLM Frameworks, Evaluation & Observability; - 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 and FastChat alternatives (DeepSpeed markdown twin, FastChat markdown twin), 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 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; FastChat trust report.