GraphCanon updated 4w · GitHub synced 4w
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
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (1d since push)
- As of 4w
- Provenance
- Not a fork · Organization account
- As of 4w
- Security (OSV)
- No lockfile
- As of 1mo
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 · Jul 24, 2026
Categories
Tags
README
qlora_fp8: true # optional, hardware-dependent
Hardware / Requirements
- NVIDIA GPU + recent driver
- CUDA 12.8, 12.9, 13, NCCL, cuDNN
- Linux x86_64
License
Apache 2.0 — see LICENSE.
For agents
This page has a .md twin and JSON over the API.