From "Localhost" to "On-Premise": An open-source blueprint for building a privacy-first, scalable AI infrastructure with vLLM and LiteLLM.

We are currently living in the "Golden Age" of Local AI. Tools like Ollama and LM Studio have democratized access to Large Language Models (LLMs), allowing any developer to spin up a 7B parameter model on their laptop in minutes.

However, a significant gap remains in the ecosystem. While these tools are fantastic for single-user experimentation, they often encounter bottlenecks when promoted to a shared, enterprise environment.

When you try to move from a "Hobbyist" setup to a "Production" on-premise infrastructure for your team, you face different challenges:

• Concurrency: How do you serve multiple concurrent users without queuing requests indefinitely?
• Decoupling: How do you swap models (e.g., Llama 3 to Qwen 2.5) without breaking client applications?
• Governance: How do you manage API keys, log usage, and enforce budget limits?

This article explores an architectural approach to solving these problems by decoupling the AI stack. I will also introduce SOLV Stack, an open-source reference implementation I built to demonstrate this architecture.

The Architectural Shift: Decoupling Components

In traditional web development, we wouldn’t connect our frontend directly to our database. We use API Gateways and Backend services. We need to apply the same rigor to AI Infrastructure.

A production-grade Local AI system should be composed of three distinct, loosely coupled layers:

1. The Presentation Layer (UI): Where users interact (Chat interface).
2. The Governance Layer (Gateway): Where routing, logging, and auth happen.
3. The Inference Layer (Compute): Where the raw model processing occurs.

By separating these concerns, we avoid vendor lock-in and ensure scalability.

The Reference Architecture (SOLV)

To implement this philosophy practically, I created a Dockerized boilerplate called SOLV Stack. It stands for the four core components selected for their performance and enterprise readiness:

• SearXNG (Privacy-focused Search)
• OpenWebUI (The Interface)
• LiteLLM (The Gateway)
• VLLM (The Inference Engine)

Here is how data flows through the system:

1. The Inference Layer: Why vLLM?

For local development, tools like Ollama (based on llama.cpp) are excellent. However, for a shared infrastructure, throughput is king.

I chose vLLM for this stack because of its PagedAttention technology. In a multi-user scenario, vLLM manages GPU memory much more efficiently than standard loaders, allowing for higher continuous batching. It is designed to be a server first, maximizing the utilization of your expensive GPUs (like the RTX 5090).

2. The Gateway Layer: The Power of LiteLLM

This is perhaps the most critical component for an enterprise architecture. LiteLLM acts as a universal proxy.

It normalizes all inputs to the OpenAI standard format. This means your client applications (whether it’s OpenWebUI, a custom React app, or an IDE plugin like Continue) only need to know how to speak "OpenAI." They don’t need to know if the backend is running vLLM, Azure, or Anthropic.

This enables a Hybrid Architecture:

• Routine tasks: Route to local vLLM (Zero cost, 100% privacy).
• Complex reasoning: Route to GPT-4 (Pay per token).

This logic is handled strictly at the config level, not in your application code.

3. The Interface: OpenWebUI

Currently, OpenWebUI offers the most comprehensive feature set for teams, including RAG (Retrieval Augmented Generation) pipelines, user role management, and chat history. Because our stack is decoupled, if a better UI comes out next year (e.g., LibreChat), you can swap this layer without touching your backend models.

Implementation: The SOLV-Stack Boilerplate

I have packaged this entire architecture into a docker-compose setup that supports NVIDIA GPUs on both Linux and Windows (WSL2)—a crucial feature for organizations where developers work on Windows machines.

Configuration Example

The magic happens in the litellm_config.yaml. Here, we map our internal vLLM instance to a user-facing model name:

model_list:
# The client sees "gpt-4-local"
- model_name: gpt-4-local
litellm_params:
# But we route it to our local Qwen 2.5 instance
model: openai/qwen2.5-coder
api_base: http://vllm-backend:8000/v1
api_key: EMPTY

Real-World Use Case: The Private Coding Assistant

One of the most immediate benefits of this stack is enabling AI coding assistants for your team without sending code to the cloud.

1. Deploy SOLV Stack on a local server with an RTX 5090.
2. Developers install the Continue or Cline extension in VS Code.
3. Point the extension to http://your-server:8080/llm/v1.
4. Result: A Copilot-like experience that runs entirely within your firewall.

Conclusion

Building a local AI platform is not just about downloading model weights; it’s about designing a system that is stable, observable, and adaptable.

By moving from a monolithic "localhost" tool to a decoupled architecture using vLLM and LiteLLM, you gain control over your data and your infrastructure.

If you want to try this architecture yourself, I’ve open-sourced the setup here. It includes scripts for model downloading, Nginx configuration, and RAG pipelines setup.

Repo: github.com/chnghia/solv-stack

I’d love to hear how you are architecting your local AI stack. Are you using a Gateway pattern? Let me know in the comments!