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How much can LoRA reduce my training costs?
LoRA typically reduces trainable parameters by 10,000x and GPU memory requirements by 70%, leading to cost savings of 95-98% compared to traditional fine-tuning methods.
What models does LoRA support?
LoRA works with most transformer architectures including GPT, BERT, T5, LLaMA, and vision transformers. We also support custom architectures and multimodal models.
How long does implementation take?
Implementation timelines vary by project complexity. Simple fine-tuning projects can be completed in 1-2 weeks, while custom enterprise solutions may take 1-3 months.
Do you provide ongoing support?
Yes, we offer comprehensive support packages including monitoring, optimization, updates, and 24/7 technical assistance for enterprise clients.
Can LoRA work with my existing infrastructure?
Absolutely. LoRA is designed to integrate seamlessly with existing ML pipelines and can be deployed on any major cloud platform or on-premises infrastructure.
What about model performance?
LoRA maintains 95-99% of original model performance while dramatically reducing computational requirements. In many cases, we see performance improvements due to reduced overfitting.
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