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February 2025 • 12 min read

Open-Source LLMs in 2025: What to Run Locally (and What to Serve in Production)

The biggest shift in 2025 isn’t that models got bigger. It’s that the open ecosystem got practical. You can now run surprisingly capable LLMs locally, and you can serve them in production without burning your team on infrastructure.

open-source-llms llama.cpp vllm ollama

TL;DR: Use LM Studio / Jan for beginners, Ollama for developer workflows, llama.cpp for maximum control, and vLLM when you’re serving real users at scale.

First: stop thinking “model” — think “stack”

Most teams fail with open-source LLMs because they pick a model first and then scramble to figure out deployment. In 2025, the winning approach is to decide:

  • Where the model runs: laptop, workstation, server, or GPU cluster
  • How you serve it: desktop UI, OpenAI-compatible API, or high-throughput inference
  • How you ground it: RAG + document pipelines + permissions

Local inference: the “fast path” for teams

Beginners: LM Studio or Jan

If your goal is to get the team experimenting quickly without turning everyone into DevOps, use a desktop tool. You can iterate on prompts, test models, and validate workflows before you build anything permanent.

Developers: Ollama

Ollama is popular because it’s simple, scriptable, and exposes a stable OpenAI-compatible API. That makes it a strong default for internal tooling and quick prototypes.

Power users: llama.cpp

If you care about maximum control over quantization, memory, and performance tuning, llama.cpp is still a foundation layer in 2025.

Production inference: where vLLM wins

When you move from “a couple of users” to “a real internal service,” throughput and concurrency matter. vLLM is widely used for production-grade inference with higher performance characteristics than typical desktop stacks.

A practical selection rubric (what we use)

  • Latency target: is this chat, batch summarization, or agent workflows?
  • Concurrency: 3 users vs 300 users are completely different designs
  • Context length: if you do RAG, you need room for retrieved documents
  • Operational effort: who will own this stack after launch?

How we ship “private AI” without chaos

Our typical approach is:

  • Prototype locally (desktop tools / Ollama)
  • Stabilize prompts + workflows
  • Move to production inference (vLLM) with observability and access controls
  • Wrap with a UI like Open WebUI + RAG for internal knowledge

Want help choosing and deploying?

If you want to run open-source LLMs privately, with reliable performance and a clean user experience for your team, contact us. We’ll map your requirements and design a stack you can actually maintain.

Sources referenced for recommendations: 2025 local LLM hosting comparisons (Ollama/vLLM/LM Studio/Jan) and open-source model ecosystem overviews.