RegiaYoung / SlideFormer
An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU (DAC ‘26)
Why this job: works in the areas we cover, but does not say which job it does
What it does
- Layer-sliding execution. keeps only a compact active layer window on the
- Lightweight asynchronous engine. overlaps GPU computation with CPU
- Heterogeneous memory hierarchy. coordinates GPU memory, CPU RAM, and
- Memory-efficient implementation. uses pre-allocated GPU cache units,
Get started
pip install packaging ninja==1.13.0
pip install --no-build-isolation -r requirements-models.txt- Maintenance
- Active
- Last commit 12 days ago
- Stars
- 138
- On GitHub
- Licence
- Apache-2.0
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- None yet
- No releases
- Built with
- Python
- 84% of the code
Is it looked after?
- Contributors
- 1
- Last 90 days
- Issues
- None
- No issues filed
- Pull requests
- 0 merged
- 0 waiting
- Releases
- None
- Never published
- No contributing guide
- No security policy
- No code of conduct
More: languages, topics and project record
Languages
- Python 84%
- C++ 11%
- Shell 2%
- Cuda 2%
Topics
- cpu-offload
- fine-tuning
- heterogeneous-systems
- llm
- nvme-storage
- pytorch
Project record
- JobOther AI tools
- StatusListed
- Commits9
- Forks5
- Watching0
- Stars in 28 days+1
- CreatedFeb 24, 2026
- First seen by usNov 7, 2026
- CheckedNov 7, 2026