How do you serve many fine-tuned LoRA adapters efficiently?
hardAnswer
- Instead of separate models, share base weights + swap adapter per request.
- Techniques: (1) Multi-LoRA serving (vLLM, SGLang, LoRAX): dynamically apply adapter matrices per request.
- (2) Batched multi-adapter: different requests in same batch can use different adapters.
- (3) Adapter storage: MB per adapter vs GB per full fine-tune.
- Scale: serve 100+ specialized models on single GPU.
- Modern pattern for enterprise (per-customer fine-tune).
- Predibase LoRAX, vLLM LoRA support.
Check yourself — multiple choice
- Random
- Share base + swap adapter per request: multi-LoRA (vLLM/SGLang/LoRAX) dynamic apply + batched multi-adapter + MB per adapter vs GB full; serve 100+ specialized on single GPU; enterprise per-customer
- Separate models
- Not real
Multi-LoRA serving: share base + swap adapter; 100+ models on 1 GPU.
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