A team fine-tuned five separate small models for five separate tasks and then discovered they couldn't afford to host five GPU instances. The fix: one base model, five LoRA adapters, swapped per request.
How it works
Because a LoRA adapter is a small set of additional weights layered onto a frozen base model, several adapters can be loaded in the same process and swapped — or even batched together — per incoming request, instead of loading five entire fine-tuned models into memory.
- Serving frameworks built for this (adapter-aware inference servers) batch requests across different adapters efficiently.
- Swap latency for a single adapter is typically low enough for interactive use, but benchmark it on your own hardware before committing.
- This only works if all five tasks share the same base model — you can't mix adapters trained on different base models.
See merging LoRA adapters and serving for the alternative — merging one adapter permanently — and when that's the better choice instead.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
