# The Architecture for Serving 100 Fine-Tuned Models on One GPU

DevFeed: [The Architecture for Serving 100 Fine-Tuned Models on One GPU](<https://devfeed.tech/articles/the-architecture-for-serving-100-fine-tuned-models-on-one-gpu-18244.md>)

Original publisher: [Read original article](<https://blog.dailydoseofds.com/p/the-architecture-for-serving-100>)

Author: Avi Chawla

Published: 2026-09-11T21:25:15Z

Content type: tutorial

Language: en

Sources: [Daily Dose of Data Science](<https://devfeed.tech/sources/daily-dose-of-data-science.md>)

Topics: [GPU](<https://devfeed.tech/topics/gpu.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [lora](<https://devfeed.tech/topics/lora.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [code](<https://devfeed.tech/tags/code.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [lora](<https://devfeed.tech/tags/lora.md>), [memory](<https://devfeed.tech/tags/memory.md>), [models](<https://devfeed.tech/tags/models.md>), [production](<https://devfeed.tech/tags/production.md>), [serverless](<https://devfeed.tech/tags/serverless.md>), [vllm](<https://devfeed.tech/tags/vllm.md>), [workers](<https://devfeed.tech/tags/workers.md>)

## AI overview

This tutorial compares architectures for serving 100 fine-tuned 7B model variants on GPUs. It explains how separate merged models increase storage, GPU memory use, scaling pools, cold starts, and idle capacity, while a shared base model with LoRA adapters enables adapter reuse through vLLM. The article plans to test merged, unmerged startup-loaded, request-time adapter loading, and hosted-per-tenant deployments on Runpod Serverless.

## Source excerpt

...explained with code.