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You can run an AI model locally by installing a model runner, downloading compatible model weights, loading those weights into your computer’s memory, and sending prompts through a desktop chat window, command line, or local API. The runner is the software that executes the model; installing it alone does not automatically install every model.
“Open-source” is an imprecise label for many AI models. Some publish source code, some publish only downloadable weights, and licenses differ. Before using a model for commercial work, inspect that model’s own license and acceptable-use terms. LM Studio explains this distinction in its official getting-started documentation.
What you need to run a model locally
A local setup has four moving parts:
- A runner: software such as LM Studio, Ollama, or llama.cpp that loads weights and performs inference.
- Model weights: the files containing the trained parameters. Common formats include
.ggufand.safetensors. - System memory and compute: RAM, GPU memory, CPU cores, or an accelerator capable of holding and processing the model.
- Storage: enough free disk space for the model download, temporary files, and any additional models you keep.
When you submit a prompt, the runner loads the weights and other model parameters into available memory, processes the text using your CPU or supported accelerator, and returns generated tokens. Your prompt does not need to be sent to a hosted chatbot service for this local interaction. If you expose a local API to another application or device, that traffic follows the network settings you configure; local execution alone does not guarantee that an API is safely isolated.
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No cited documentation establishes a universal fastest or best runner. The practical choice depends on installation effort, interface, model format, hardware backend, and whether you need command-line or API integration.
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| Runner | Best fit | How you start | Formats and hardware notes | Integration |
|---|---|---|---|---|
| LM Studio | People who want a graphical model browser and chat interface | Install the app, use Discover to obtain a model, open Chat, select the downloaded model in the loader, and start a conversation | Check the current system requirements and the selected model’s supported format; loading allocates memory for weights and related parameters | Primarily a desktop workflow; consult current documentation for available application or server features |
| Ollama | People who prefer a simple command and a local service | Install for macOS, Windows, Linux, or Docker, then run ollama run llama3.2 |
Model availability and size vary; the quickstart includes examples from under 1 GB to hundreds of GB | Provides commands for managing models and documented local REST endpoints |
| llama.cpp | Users who want direct CLI control, a server, or build-time configuration | Install through a package manager, Docker, a prebuilt binary, or a source build, then use llama-cli or llama-server |
Requires GGUF files and documents CPU, GPU, accelerator, and hybrid CPU/GPU backends | llama-server provides a local OpenAI-compatible HTTP endpoint |
Read the runner’s current requirements before downloading a large model. LM Studio’s workflow and licensing cautions are described at lmstudio.ai/docs/app/basics. Ollama’s supported installation routes, model examples, and API information are in its Quickstart. Installation methods, GGUF requirements, hardware backends, CLI usage, and the local server are documented in the llama.cpp README.
Check whether your computer can hold the model
Model size affects both download time and memory use. The following figures are examples published in Ollama’s quickstart, not universal minimum requirements. Actual demand changes with the model format, quantization, context length, runtime, accelerator, and other applications running at the same time.
| Example model | Published download size | How to interpret it |
|---|---|---|
| Moondream 2 | 829 MB | A comparatively small starting point for testing a local workflow |
| Llama 3.2 3B | 2.0 GB | Small enough for many modern computers, subject to runtime overhead |
| Mistral 7B | 4.1 GB | A larger general-purpose option that needs more memory than a 3B model |
| Llama 3.1 70B | 40 GB | Usually requires substantial RAM or GPU memory and careful configuration |
| Llama 3.1 405B | 231 GB | A very large download that is impractical for many personal computers |
Ollama’s quickstart gives these starting points for available RAM: at least 8 GB for 7B models, 16 GB for 13B models, and 32 GB for 33B models. Treat those numbers as practical guidance rather than guarantees. A model can fail to load even when its download appears smaller than your installed RAM because the operating system, context window, runtime buffers, and other programs also consume memory.
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- Check free RAM and, if applicable, dedicated GPU memory while your normal applications are open.
- Compare that capacity with the model’s published file size and the runner’s current requirements.
- Leave headroom for the operating system and context. Do not plan to use every available gigabyte for weights.
- Begin with a smaller model. Move up only when it loads reliably and its quality is insufficient for your task.
An external SSD can help when internal storage is limited because model files can be much larger than ordinary applications. It is optional and does not, by itself, demonstrate faster inference. Check the exact download size and available disk space before buying storage.
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Understand formats, quantization, and compatibility
The runner and the model must agree on a supported file format. A model advertised as .safetensors is not automatically usable by every local runtime. llama.cpp specifically requires GGUF files and includes conversion and quantization tools in its project documentation. Other runners may support additional formats.
Quantization stores model values with fewer bits. The resulting file generally uses less memory and can run on a smaller computer, but quality and behavior can change. Quantization is not a free compression step: evaluate the quantized variant on the prompts that matter to you, especially if you need precise coding, long-context reasoning, or reliable structured output.
Compatibility checklist
- Confirm the model file format is supported by your chosen runner.
- Check whether the download is a complete model or an adapter that requires another base model.
- Read the model card for context-length limits, hardware notes, and usage restrictions.
- For llama.cpp, use a GGUF file or convert the model with the project’s documented tools before attempting to load it.
- Keep the original download until you have verified that the selected quantization works for your workload.
Step-by-step: install and run your first local model
1. Decide how you want to interact
Choose a graphical chat application if you want visual model discovery and minimal command-line work. Choose Ollama if a short command and a local service suit you. Choose llama.cpp if you need explicit CLI flags, backend selection, or a controllable server process. Your choice can change later; model files and licenses remain the important compatibility constraints.
2. Verify current system requirements
Before downloading weights, open the runner’s current requirements page and check your operating system, CPU architecture, available RAM, GPU or accelerator support, and disk space. LM Studio directs users to check system requirements. llama.cpp documents multiple CPU and accelerator backends, while Ollama provides installation routes for macOS, Windows, Linux, and Docker. Requirements can change as releases add or remove backend support.
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3. Select a model and read its terms
Pick a model whose capability, size, format, and license match your task. “Open-weight” does not mean that every use is permitted. Some licenses impose attribution, redistribution, acceptable-use, or commercial conditions. Record the exact model name and version you downloaded so you can audit or reproduce the setup later.
4. Download enough disk space
Download through the runner’s model browser or documented command. Large models may occupy tens or hundreds of gigabytes, and a failed download can leave temporary files. Keep extra space for updates and for a second model if you plan to compare behavior.
5. Load the weights and send a short prompt
Use a small, observable test rather than beginning with a long document. Ask the model to identify itself, summarize a short paragraph, or return a three-item list. This confirms that the file loaded, the context is working, and output is being generated before you spend time debugging a complex application.
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6. Repeat with a task-specific test
Use prompts representative of your real work: a code snippet for programming, a known document for summarization, or a fixed set of questions for extraction. Compare factual accuracy, formatting, latency, and memory use. A model that loads quickly may still be unsuitable if it fails your required task.
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Runner-specific instructions
LM Studio: graphical installation and chat
- Install LM Studio for your operating system.
- Open Discover and find a model whose format, license, and size fit your computer.
- Download the model and wait for the file to finish completely.
- Open Chat, use the model loader to select the downloaded model, and start a conversation.
- Watch memory usage while loading. The application allocates memory for model weights and other parameters; insufficient memory can prevent loading or force heavy swapping.
The labels above follow the workflow in LM Studio’s documentation. If a model does not appear in the loader, check its format and whether the current application release supports it rather than assuming the download is corrupt.
Ollama: command-first setup
- Install Ollama using the route for your operating system or Docker environment.
- Run
ollama run llama3.2to obtain and start the documented example model. - Send a short prompt at the interactive command line.
- Use Ollama’s documented model-management commands to see what is installed and to remove models you no longer need.
- When an application needs programmatic access, follow the documented local REST API instructions and confirm which interface and network address your application uses.
The command downloads model data as needed, so the first run can take longer than subsequent prompts. If the model will not load, compare its size and your available memory with the capacity guidance rather than repeatedly retrying the same oversized model.
llama.cpp: direct CLI or local server
- Install a package, prebuilt binary, Docker image, or source build according to the project README.
- Obtain a compatible GGUF model file.
- Run the command-line client with the model path, for example
llama-cli -m my_model.gguf. - Enter a short test prompt and observe whether the model loads without memory errors.
- For application integration, start the documented
llama-serverwith your model and connect your client to its local OpenAI-compatible HTTP endpoint.
llama.cpp can use CPU, GPU, or hybrid CPU/GPU inference depending on the build and backend. A binary built without the backend your hardware needs may run slowly or fail to use the accelerator, so verify the build instructions for your platform.
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Connect local inference to an application
A local API lets a script, editor, or internal tool submit prompts to the runner instead of opening a chat window. Ollama documents local REST endpoints, and llama.cpp documents an OpenAI-compatible HTTP server. The exact request path, port, authentication behavior, and model name depend on the runner and its current configuration; use the runner’s own API documentation rather than copying settings from an unrelated installation.
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Keep the endpoint local by default
- Bind the service to localhost unless another device genuinely needs access.
- Do not expose a listening port to the internet without authentication, encryption, and a firewall policy appropriate to your network.
- Check which interfaces the process binds to after changing configuration; an API being available does not prove that it is private.
- Review application logs for prompts, file paths, or sensitive data before sharing them.
Local inference can reduce the need to transmit prompts to a hosted service, but it does not automatically remove privacy risks. A browser extension, plugin, or connected application can still copy prompts or send results elsewhere.
Troubleshoot the common failure modes
The model will not load
- Insufficient memory: close other applications, choose a smaller model, or use a more aggressively quantized file.
- Wrong format: obtain a file supported by the runner; llama.cpp requires GGUF.
- Unsupported backend: install a build that supports your CPU, GPU, or accelerator, or run on the CPU if performance is acceptable.
- Incomplete download: remove the damaged file and download it again from the runner or model publisher.
Generation is extremely slow
- Check whether the runner is using the intended GPU or accelerator instead of falling back to the CPU.
- Reduce model size or context length if memory pressure causes swapping.
- Close memory-heavy applications and test a smaller prompt.
- Compare a quantized model with the original only after verifying that quality remains acceptable.
Output quality is poor
- Confirm that the model is intended for your language and task.
- Use a clear prompt with the desired format and a short example.
- Test a larger model if your computer can sustain it, or select a model tuned for instruction following or coding.
- Check the model’s context limit; truncation can remove the instructions or source text the model needs.
An application cannot reach the API
- Verify that the runner process is running and listening on the address your application uses.
- Confirm the model name exactly matches the installed name.
- Check firewall rules and whether the service is bound only to localhost.
- Read the current runner API documentation for the correct endpoint and request format.
Licensing, updates, and responsible use
Save the model’s license text and download identifier alongside your project notes. A runner’s permissive license does not change the terms of the model weights. If you redistribute weights, build a commercial product, or generate content for clients, verify the specific model’s restrictions and attribution requirements.
Update the runner and model deliberately rather than automatically replacing a working setup. A new quantization, prompt template, tokenizer, or runtime version can change output quality and memory use. Keep a small regression test—several representative prompts with expected formatting—so you can detect a behavior change after an update.
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A practical selection framework
- Start with the task: define whether you need chat, coding, summarization, extraction, or an API.
- Set a memory ceiling: use available RAM and GPU memory, leaving operating-system headroom.
- Choose the runner: graphical workflow for LM Studio, command-first service for Ollama, or configurable CLI/server control for llama.cpp.
- Match the format: verify that the exact weight file is supported; use GGUF for llama.cpp.
- Validate quality: run representative prompts before adopting the model.
- Secure integration: keep API access local unless you have deliberately configured and protected network access.
This process avoids the most expensive mistake: downloading a model that cannot fit your computer or cannot legally be used for your intended purpose.
What “running locally” does—and does not—promise
Local inference means the selected runner performs generation on your computer. It does not promise unlimited model size, instant responses, perfect accuracy, or automatic privacy for every connected tool. Performance depends on the model, quantization, context, hardware backend, and competing applications. The only reliable way to choose between two workable models is to test both on the prompts and constraints that define your project.
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