Multi-LoRA serving

Multi-LoRA serving is the inference pattern where one base model serves dozens or hundreds of LoRA adapters from a single deployment — Predibase LoRAX, vLLM multi-LoRA, S-LoRA pioneered this. The cost-efficient way to deploy per-tenant fine-tunes.

Without multi-LoRA, each fine-tuned variant needs its own deployment (each costing > $1K/mo for a serious GPU). Multi-LoRA serving fits the LoRA computation into the same forward pass as the base model: load the base once, swap small (10-100 MB) LoRA matrices per request based on routing. Implementations: vLLM multi-LoRA (Apache 2.0), Predibase LoRAX (per-request LoRA selection), S-LoRA (paper / research). Production unlock: thousands of per-customer fine-tunes served from one GPU pool at the marginal cost of base + adapter swap. Trade-offs: throughput drops vs single-LoRA, adapter swapping has overhead, complex routing logic.

When to use multi-lora serving

• Multi-tenant fine-tunes (per customer / use case).
• Cost-sensitive LoRA serving at scale.

Common mistakes

• Multi-LoRA on tiny base — adapter swap overhead dominates.

FAQ

Related terms

• LoRA fine-tune — LoRA (Low-Rank Adaptation) fine-tune is the parameter-efficient method that trains small adapter matrices on top of frozen base weights — 10-100× cheaper than full fine-tune, swappable per task, easy to serve many LoRAs from one base model.
• LoRA hot-swapping — LoRA hot-swapping is the serving pattern where many fine-tuned LoRA adapters share a single base model on GPU — the appropriate adapter is loaded per request without reloading the base model.
• Inference engine — An inference engine is the optimized runtime that loads model weights and serves predictions — vLLM, TGI, TensorRT-LLM, sglang, llama.cpp are 2026 leaders. Different engines specialize: throughput, latency, multi-LoRA, on-device, batching.

Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/multi-lora-serving.md.