How do you serve many fine-tuned variants (e.g. per-customer LoRA adapters) efficiently?

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Use multi-LoRA serving: load one shared base model on the GPUs and dynamically apply lightweight LoRA adapters per request (supported by engines like vLLM and others). This avoids a dedicated GPU deployment per fine-tune. Manage adapters in a registry, load and unload them on demand with an LRU cache, route requests carrying the adapter ID, and monitor per-adapter quality and usage. Merge adapters into the base weights only for very high-traffic variants that need maximum speed.

Architecture

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Request {tenant: "acme", adapter: "acme-support@v3"} → gateway → inference pool
Inference pool: base model (e.g. 8B) + adapter cache in GPU/CPU memory
- Hot adapters resident in GPU memory
- Warm adapters in CPU memory; cold ones loaded from the registry on demand

Benefits: hundreds of variants on a few GPUs; fast onboarding of new adapters; cost-efficient per-tenant customisation.

Considerations

Concern	Approach
Latency of cold adapter loads	Pre-load popular adapters; async loading; small adapter sizes
Batching across adapters	Engines batch requests for different adapters together (with some overhead)
Base model upgrades	Adapters are tied to a base version, so retrain or revalidate when the base changes
Isolation	Tenant A must never get tenant B's adapter; enforce mapping server-side
Quality monitoring	Evals per adapter; detect regressions after retraining
Rank limits	Engines often cap max LoRA rank; plan adapter configs accordingly

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How do you serve many fine-tuned variants (e.g. per-customer LoRA adapters) efficiently?

Architecture

Considerations

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