Fifty teams have fine-tuned the same base model. How do you serve all of them?

Group compatible adapters by pinned base, derive their size from target matrices and ranks, and benchmark mixed batches. Catalogue size, CPU residency, GPU slots and active adapters are separate limits; base sharing does not make fifty variants operationally free.

Updated Sep 2026 · Learn how AI infrastructure works through explanations, worked examples and diagrams, then practise applying it to interview questions.

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Work through the numbers

Change the assumptions in a free calculator, then explain what the result establishes and what still needs measurement.

The concepts behind this question

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🚀 Inference & Serving🔒 Premium

Multi-LoRA ServingMulti-LoRA serving applies different adapters to a shared base model. It saves weight memory while adding adapter loading, scheduling and isolation requirements.

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📐 AI Systems Design🔒 Premium

Multi-Tenant Fine-Tuning ServiceA fine-tuning service turns a tenant’s dataset into an evaluated model or adapter. Its pipeline must protect data while scheduling training and serving the result efficiently.

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🧮 Open Weights & Serving Engines🔒 Premium

Pretraining, Fine-Tuning and DistillationUnderstand how a base model becomes a useful application model. Separate training objectives from LoRA and quantization, compare distillation with retrieval, and choose the smallest intervention supported by your evaluation.

Core

🚀 Inference & ServingSign in

Continuous BatchingContinuous batching changes which requests run between model iterations. Learn how a waiting request joins, why an empty slot is not enough, and how to balance throughput against each user’s token latency.

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UP NEXT ON YOUR JOURNEY

Next in this trackA replica restarts and loads different weights than its siblings. How did that happen?Next in this trackYou must serve a frontier open-weights model on non-NVIDIA accelerators. Plan it.Next in this trackYour model's weights fit in GPU memory, but the engine cannot load them. How do you inspect the model package?

FEDITOR'S NOTE

Scored on exact LoRA sizing, compatible base identity, mixed-batch execution costs, runtime slot limits and secure versioned adapter lifecycle.

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