6 alternatives to Ollama for running an LLM locally
Ollama made running language models locally accessible to everyone. But once you are past the first "it works", other tools answer certain needs better: a polished interface, fine-grained control over performance, multi-user service. Here is a tour of six alternatives, and of the moment when you leave the workstation for the sovereign server.
Ollama is excellent for getting started: one command, one model, an OpenAI-compatible API. But it is not the only path. This article draws on a round-up published by XDA Developers and puts it in perspective with what really matters when you move from personal use to an organisation-wide deployment.
The six tools at a glance
| Tool | Nature | Interface | OpenAI API | Audience |
|---|---|---|---|---|
| LM Studio | Desktop application | Graphical | Yes | Beginners, quick trials |
| llama.cpp | Inference engine | Command line | Yes (server) | Maximum control, minimal footprint |
| KoboldCpp | Portable executable | Web + API | Yes | On the move, all audiences |
| Jan | Desktop application | Graphical (ChatGPT style) | Yes | Polished UX, local + cloud |
| vLLM | Serving engine | API | Yes | Production, multi-user |
| Msty AI | Workspace | Graphical | Via providers | Productivity, local + cloud |
1. LM Studio: the gentle way in
A desktop application with a graphical interface for browsing, downloading and launching models in a few clicks. LM Studio exposes an OpenAI-compatible API and supports hundreds of models on a wide range of hardware. Ideal for exploring without touching the terminal; a little less lightweight than command line tools.
2. llama.cpp: the engine under the bonnet
This is the open source inference engine that powers much of the ecosystem: Ollama itself is built on it. From the command line, it detects the hardware, picks the optimal quantisation and decides on its own how many layers to offload to the GPU. Minimal footprint, best output per watt, total control. The price to pay: being comfortable with a terminal.
3. KoboldCpp: the portable Swiss army knife
A single executable, with no installation, that can be launched from a USB stick. Built on llama.cpp, it adds a web interface and an API, runs on CPU as well as GPU and reads the GGUF format. Express deployment, suitable for beginners and advanced users alike.
4. Jan: the "ChatGPT" experience, locally
A modern desktop application designed around the experience: a ChatGPT-style interface, model switching without commands, and the option of plugging in cloud providers alongside local models. OpenAI-compatible API. What you gain in comfort you lose in low-level tuning compared with llama.cpp.
5. vLLM: the production engine
Here we change category. vLLM is not made for "chatting" from a desktop: it is a high-throughput serving engine. Handling many simultaneous requests at low latency, optimised memory management (PagedAttention), continuous batching, it targets teams building AI applications and serving tens to hundreds of users. It is the engine we run in production for our sovereign deployments.
6. Msty AI: the unified workspace
A single interface that orchestrates both local providers (Ollama, LM Studio and others) and cloud services (OpenAI, Anthropic, Google). Prompt libraries, conversation organisation, document analysis, web search: a "productivity" approach for anyone juggling local and cloud, at the price of a richer feature set that is more complex than minimalist tools.
Local to experiment, server for the organisation
All these tools share one virtue: your data stays on your machine. For individual use that is perfect: coding, summarising, testing a model. You are even spoilt for choice depending on whether you prioritise the interface, the control or the portability.
But serving an entire organisation changes the equation, exactly as we set out for local AI on the Mac:
• Concurrency: a workstation properly serves one request at a time. 50, 200 or 340 concurrent users call for server GPUs and an engine designed for throughput, hence vLLM.
• Availability and governance: high availability, monitoring, backup, SSO, documented GDPR compliance and traceability do not apply to individual machines.
• Sizing: choosing the right model, the right quantisation and the right GPU is a calculation, which is the purpose of our VRAM calculator.
In other words: these alternatives to Ollama are excellent testing grounds. For an AI that is sovereign, at scale and governed, the answer is a serving engine (vLLM) on controlled GPU infrastructure, which is what we deploy and operate for local authorities and public institutions.
From the local tool to sovereign production
The same philosophy, your data stays with you, but sized for your whole organisation, with RAG, SSO and monitoring.
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