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Multi-LoRA Serving

One base model in memory, a thousand fine-tunes on top — not a thousand full copies of the model.

Key Insight​

This project stands up a multi-adapter engine (Lorax or S-LoRA), trains 5 small LoRA adapters, and serves them all from a single copy of the base model, then compares throughput against running 5 separate replicas. The trick is batching requests that use different adapters into one forward pass — see multi-LoRA.

Why This Matters​

Giving every customer their own fine-tuned model would normally mean one GPU deployment per customer, which does not scale. Because each adapter is tiny (megabytes) while the base model is large, sharing one base across hundreds of adapters is the economic feature that makes per-tenant fine-tuning affordable for SaaS products.