Start with LM Studio if a model browser and visual settings help you. Choose Ollama if you prefer named models and a service your apps can call. Choose llama.cpp if you need explicit GGUF files, backend choices, and detailed command-line control. All three require a compatible model and enough usable memory.
Compare the decisions that matter
| Decision | LM Studio | Ollama | llama.cpp |
|---|---|---|---|
| Typical starting workflow | Desktop model search, download, load, and chat | Library tag, command, and local service | Choose a build/backend and a model file |
| Model management | Visual management of supported local formats | Named tags and imported models | Explicit GGUF file paths or supported repository downloads |
| Local app integration | Local server options | Local API | llama-server |
| Why choose it? | Visible settings and an integrated chat workflow | Repeatable model names and app integration | Direct configuration and backend control |
| What still needs checking? | Engine and model support | GPU/backend and tag contents | Build flags, backend, and model format |
These describe useful entry points, not exclusive capabilities. Features change; use the official documentation linked below for exact current commands and platform requirements. A familiar interface does not remove the need to check what is actually running.
Runtime, model family, and model file are different
Llama and Qwen are model families. GGUF is a model-file format supported by llama.cpp and other runtimes. Ollama provides model tags and management around supported inference engines. LM Studio provides an application and engine integration. A model name in one tool does not guarantee an identical file in another.
For a fair comparison, record the exact model revision, quantization, context, hardware backend, and accelerator placement. Two downloads labeled “8B” may have different precision, chat templates, or defaults. Those differences can explain an apparent performance gap.
Check the backend before the interface
“Runs on Windows” does not mean every Windows GPU is accelerated. CUDA, Metal, Vulkan, ROCm, and SYCL are different execution paths with different device and build requirements. An app may fall back to CPU computation when a usable accelerator backend is unavailable.
Consult Ollama’s hardware support, LM Studio’s system requirements, and the llama.cpp supported backends. For an AMD or Intel GPU in particular, check the named runtime and OS combination before buying hardware. MyLocalRig’s memory fit is a separate planning calculation.
A five-step comparison you can repeat
- Pick one instruction-tuned model and document its exact weight format and quantization.
- Use the same context budget and a short, fixed prompt.
- Load one model at a time; competing processes can consume accelerator memory.
- Inspect GPU offload in the runtime’s status or logs. In Ollama, use
ollama ps. - Record load time, time to first output, generation speed, and whether the answer is useful. Treat a cold load separately from an already-loaded model.
Do not choose solely by tokens per second. If your work involves long documents, prompt processing and context capacity may matter more. If you only need a short daily chat, installation and model management may matter more than tuning every backend option.
Offline operation depends on the complete workflow
Download the model and any required engine files before disconnecting. LM Studio documents local chat and document processing without a network connection. A cloud model, external tool, or integration can introduce a network dependency in an otherwise local setup. Test the particular task you intend to keep offline rather than assuming the app name proves it.
Make one working setup first
There is no need to install every runtime before using local AI. Choose one, load a small supported model, verify placement, and test a real task. Move to another runtime when you can name the missing capability. Keep your test prompt and model details so that comparison answers a useful question.
Sources and review notes
Reviewed October 21, 2026. Official documentation and publisher data support the setup details. Memory examples are calculations, not firsthand benchmark results. Versions, defaults, and hardware support can change.