Efficient PEFT Methods with Adaptive Checkpointing for Vision Models and VLMs on Resource Constrained Consumer-GPUs
Modern pretrained vision models achieve strong accuracy but demand substantial GPU memory for fine-tuning, making edge deployment impractical. This paper compares five parameter-efficient fine-tuning (PEFT) methods (Full FT, LoRA, AdaLoRA, QLoRA, BitFit) on Transformers- (ViT-Small, TinyViT) and Mamba-based vision backbones (Vim-Small, MambaVision-T) under an on-device VRAM budget (e.g., 2 GB), together with three gradient-checkpointing strategies (none, static, and a proposed memory-budget-aware adaptive algorithm); and we evaluate three families of foundation-model baselines: zero-shot contr
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- PossiblePossibly related (embedding) · 48%open-edge-platform/geti →
- PossiblePossibly related (embedding) · 46%DoubangoTelecom/compv →
- PossiblePossibly related (embedding) · 46%Going from single GPU to dual GPU is nice but not in the way I expected →
- PossiblePossibly related (embedding) · 46%NVIDIA-NeMo/Curator →
- FuzzySimilar title/name (fuzzy) · 59%VioletVision-3B →
“Fuzzy title match (0.73): “Efficient PEFT Methods with Adaptive Checkpointing for Visio” ≈ “VioletVision-3B””
- PossiblePossibly related (embedding) · 47%NVIDIA/TransformerEngine →
- LinkedLinked via arxiv author · 85%Altay Toktassyn →
“Efficient PEFT Methods with Adaptive Checkpointing for Vision Models and VLMs on Resource Constrained Consumer-GPUs”
- LinkedLinked via arxiv author · 85%Jurn-Gyu Park →
“Efficient PEFT Methods with Adaptive Checkpointing for Vision Models and VLMs on Resource Constrained Consumer-GPUs”