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.