--- title: "DeepSpeed vs surogate" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepspeedai-deepspeed-vs-invergent-ai-surogate" tools: ["deepspeedai-deepspeed", "invergent-ai-surogate"] --- # DeepSpeed vs surogate *GraphCanon updated Sep 20, 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 surogate if surogate is a C++-based repository that accelerates training and fine-tuning for generative AI models using CUDA on NVIDIA GPUs. [DeepSpeed](https://www.deepspeed.ai/) reports 43k GitHub stars, 5.0k forks, and 1.4k open issues, last pushed Sep 6, 2026. [surogate](https://surogate.ai) has 813 stars, 10 forks, and 6 open issues, last pushed Sep 19, 2026. Figures are from public GitHub metadata via [DeepSpeed's repository](https://github.com/deepspeedai/DeepSpeed) and [surogate's repository](https://github.com/invergent-ai/surogate). | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [surogate](/tools/invergent-ai-surogate.md) | | --- | --- | --- | | Tagline | Deep learning optimization library for efficient distributed training and inference | Training/Fine-tuning at the speed of light | | Stars | 43,065 | 813 | | Forks | 4,963 | 10 | | Open issues | 1,385 | 6 | | Language | Python | C++ | | 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. | surogate is a C++-based repository that accelerates training and fine-tuning for generative AI models using CUDA on NVIDIA GPUs | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Inference & Serving, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [surogate](/tools/invergent-ai-surogate.md) | | --- | --- | --- | | Open issues (now) | 1.4k | 6 | | Stars delta | +195 (30d) | +7 (30d) | | Open issues delta | +77 (30d) | 0 (30d) | | Full report | [trust report](/tools/deepspeedai-deepspeed/trust.md) | [trust report](/tools/invergent-ai-surogate/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: surogate - **Adopt for:** surogate is a C++-based repository that accelerates training and fine-tuning for generative AI models using CUDA on NVIDIA GPUs ## Choose when ### Choose DeepSpeed if… - DeepSpeed is primarily Python; surogate is C++. - Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, gpu. - Also covers Inference & Serving. - - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters) ### Choose surogate if… - surogate is primarily C++; DeepSpeed is Python. - Tags unique to surogate: cuda, fine-tuning, generative-ai, llama. - When needing rapid training and fine-tuning capabilities for generative AI models that take full advantage of NVIDIA GPU acceleration via CUDA. ## 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 surogate - If working in an environment without access to NVIDIA GPUs, as surogate leverages CUDA for its speed optimizations specifically designed for these hardware configurations. - When looking to use a more accessible language like Python for training and fine-tuning, since surogate is based on C++ which may offer less ease-of-use. ## Common questions ### What is the difference between DeepSpeed and surogate? DeepSpeed: Deep learning optimization library for efficient distributed training and inference. surogate: Training/Fine-tuning at the speed of light. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepSpeed over surogate? Choose DeepSpeed over surogate when DeepSpeed is primarily Python; surogate is C++; Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, gpu; Also covers Inference & Serving; - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters). ### When should I choose surogate over DeepSpeed? Choose surogate over DeepSpeed when surogate is primarily C++; DeepSpeed is Python; Tags unique to surogate: cuda, fine-tuning, generative-ai, llama; When needing rapid training and fine-tuning capabilities for generative AI models that take full advantage of NVIDIA GPU acceleration via CUDA. ### 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 surogate? If working in an environment without access to NVIDIA GPUs, as surogate leverages CUDA for its speed optimizations specifically designed for these hardware configurations. When looking to use a more accessible language like Python for training and fine-tuning, since surogate is based on C++ which may offer less ease-of-use. ### Is DeepSpeed or surogate more popular on GitHub? DeepSpeed has more GitHub stars (43,065 vs 813). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSpeed and surogate open source? Yes - both are open-source projects on GitHub (DeepSpeed: Apache-2.0, surogate: Apache-2.0). ### Where can I find alternatives to DeepSpeed or surogate? GraphCanon lists graph-backed alternatives at [DeepSpeed alternatives](/tools/deepspeedai-deepspeed/alternatives) and [surogate alternatives](/tools/invergent-ai-surogate/alternatives) ([DeepSpeed markdown twin](/tools/deepspeedai-deepspeed/alternatives.md), [surogate markdown twin](/tools/invergent-ai-surogate/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-invergent-ai-surogate.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepSpeed or surogate? DeepSpeed: Very active. surogate: 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 DeepSpeed and surogate? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSpeed trust report](/tools/deepspeedai-deepspeed/trust); [surogate trust report](/tools/invergent-ai-surogate/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/_