GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
surogate is a C++-based repository that accelerates training and fine-tuning for generative AI models using CUDA on NVIDIA GPUs
Good fit when
- When needing rapid training and fine-tuning capabilities for generative AI models that take full advantage of NVIDIA GPU acceleration via CUDA.
- For developers familiar with C++, who can leverage surogate's optimization techniques to expedite model development cycles.
Avoid when
- 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.
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of Sep 19, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 19, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/invergent-ai/surogateSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A C++ based repository focusing on high-speed training and fine-tuning for generative AI models, leveraging CUDA for NVIDIA GPU optimization.
Capability facts
- Languages
- c++, python
Source: github.language+pyproject.toml · Sep 19, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 19, 2026)
Use **Linux x86_64 and Python 3.12** with a supported NVIDIA GPU and CUDA 12.8+ or 13.x. See [hardware](#hardware)Source link
Tags
README
Install Use Linux x86 64 and Python 3.12 with a supported NVIDIA GPU and CUDA 12.8+ or 13.x. See hardware for the separate training and serving GPU targets. The installer selects a CUDA specific wheel and downloads the example configurations. On CUDA 13 the wheel is also installa...
For agents
This page has a .md twin and JSON over the API.