--- title: "DeepSpeed vs fastDeploy" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepspeedai-deepspeed-vs-notai-tech-fastdeploy" tools: ["deepspeedai-deepspeed", "notai-tech-fastdeploy"] --- # DeepSpeed vs fastDeploy *GraphCanon updated Jul 15, 2026* ## Verdict Pick DeepSpeed when license: DeepSpeed is Apache-2.0, fastDeploy is MIT; pick fastDeploy when license: fastDeploy is MIT, DeepSpeed is Apache-2.0. [DeepSpeed](https://www.deepspeed.ai/) reports 43k GitHub stars, 4.9k forks, and 1.3k open issues, last pushed Jul 13, 2026. [fastDeploy](https://github.com/notAI-tech/fastDeploy) has 105 stars, 17 forks, and 0 open issues, last pushed Feb 10, 2026. Figures are from public GitHub metadata via [DeepSpeed's repository](https://github.com/deepspeedai/DeepSpeed) and [fastDeploy's repository](https://github.com/notAI-tech/fastDeploy). | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [fastDeploy](/tools/notai-tech-fastdeploy.md) | | --- | --- | --- | | Tagline | Deep learning optimization library for efficient distributed training and inference | Deploy DL/ ML inference pipelines with minimal extra code. | | Stars | 42,700 | 105 | | Forks | 4,881 | 17 | | Open issues | 1,299 | 0 | | 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. | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Inference & Serving, Model Training | Inference & Serving, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [fastDeploy](/tools/notai-tech-fastdeploy.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 154d | | Open issues (now) | 1.3k | 0 | | Full report | [trust report](/tools/deepspeedai-deepspeed/trust.md) | [trust report](/tools/notai-tech-fastdeploy/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. ## Choose when ### Choose DeepSpeed if… - License: DeepSpeed is Apache-2.0, fastDeploy is MIT. - Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, gpu. - - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters) ### Choose fastDeploy if… - License: fastDeploy is MIT, DeepSpeed is Apache-2.0. - Tags unique to fastDeploy: docker, falcon, gevent, gunicorn. - Also covers Speech & Audio. ## 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 fastDeploy - Last GitHub push was 155 days ago (slowing maintenance, Feb 10, 2026). Validate activity before betting a new project on fastDeploy. - 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 DeepSpeed and fastDeploy? DeepSpeed: Deep learning optimization library for efficient distributed training and inference. fastDeploy: Deploy DL/ ML inference pipelines with minimal extra code.. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepSpeed over fastDeploy? Choose DeepSpeed over fastDeploy when License: DeepSpeed is Apache-2.0, fastDeploy is MIT; Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, gpu; - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters). ### When should I choose fastDeploy over DeepSpeed? Choose fastDeploy over DeepSpeed when License: fastDeploy is MIT, DeepSpeed is Apache-2.0; Tags unique to fastDeploy: docker, falcon, gevent, gunicorn; Also covers Speech & Audio. ### 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 fastDeploy? Last GitHub push was 155 days ago (slowing maintenance, Feb 10, 2026). Validate activity before betting a new project on fastDeploy. 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 DeepSpeed or fastDeploy more popular on GitHub? DeepSpeed has more GitHub stars (42,700 vs 105). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSpeed and fastDeploy open source? Yes - both are open-source projects on GitHub (DeepSpeed: Apache-2.0, fastDeploy: MIT). ### Where can I find alternatives to DeepSpeed or fastDeploy? GraphCanon lists graph-backed alternatives at [DeepSpeed alternatives](/tools/deepspeedai-deepspeed/alternatives) and [fastDeploy alternatives](/tools/notai-tech-fastdeploy/alternatives) ([DeepSpeed markdown twin](/tools/deepspeedai-deepspeed/alternatives.md), [fastDeploy markdown twin](/tools/notai-tech-fastdeploy/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-notai-tech-fastdeploy.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepSpeed or fastDeploy? DeepSpeed: Very active. fastDeploy: 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 DeepSpeed and fastDeploy? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSpeed trust report](/tools/deepspeedai-deepspeed/trust); [fastDeploy trust report](/tools/notai-tech-fastdeploy/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/_