Multi-LoRA serving: one base model, hundreds of adapters
How to serve many fine-tuned variants from one copy of the base weights: the adapter memory maths, batched kernels that mix adapters in one step, hot loading, routing, quality and latency trade-offs, and when to merge instead.
15 min·Verified 2026-111 animated diagramPro+50 XP
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Fine-tuning produces variants: one per customer, per task, per language, per persona. Deploying each as a full model multiplies GPU cost by the number of variants. LoRA fine-tuning makes a different approach possible, because the fine-tune is a small add-on to weights you already have.
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