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To run an open-source large language model (LLM) on your own computer, install a local runner, download a model that fits your available memory, and start a chat without sending prompts to a hosted service. GPT4All is the simplest point-and-click route and documents operation without a GPU; Ollama is suited to command-line and API work; LM Studio provides a graphical interface with explicit Windows, macOS, and Linux hardware requirements. Start with a small, quantized model, verify that it loads, and treat local and cloud modes as different privacy choices.
What “running an LLM locally” actually means
A local LLM setup has three separate pieces:
- Runner: the application that loads model weights, manages memory, and generates text. Ollama, LM Studio, and GPT4All are practical cross-platform runners.
- Model file: the downloaded weights, commonly in GGUF format and sometimes in Safetensors format. The file is stored on your disk and loaded into RAM and, when available, GPU memory.
- Your workload: chat, summarization, coding, document analysis, or an application calling a local API. Larger context windows and longer answers consume additional memory.
“Open-source” is not a guarantee that every model has the same permissions. GPT4All’s catalog notes that licenses vary for personal and commercial use, so read the license attached to the specific model before using its output in a product or business workflow.
Local inference can keep prompts on the computer, but only when you select a local model and avoid optional remote integrations. A runner may also offer hosted or cloud models; those send prompts to a provider’s servers and should not be treated as offline execution.
Choose a local runner before downloading a model
| Priority | Starting path | Why it fits | Trade-off |
|---|---|---|---|
| Fastest beginner GUI | GPT4All Desktop | Install the app, add a model, load it, and chat. Its documentation says no API calls or GPU are required. | The available catalog and speed are limited by the models and hardware your computer can handle. |
| Developer CLI or API | Ollama | Cross-platform installers and a documented local API path make it convenient to script and integrate. | Large models can be slow without a strong GPU, and its cloud models are separate from local execution. |
| GUI with detailed hardware guidance | LM Studio | Supports Windows, macOS, and Linux and publishes CPU, RAM, GPU, and operating-system requirements. | Current Mac support is Apple Silicon only, and older systems may not meet the requirements. |
| Maximum manual control | A direct llama.cpp-compatible workflow | You choose the runtime, model file, quantization, and launch settings yourself. GPT4All identifies llama.cpp as a backend and GGUF as a common model type. | Model selection, command-line options, memory planning, and updates are your responsibility. |
If you want a first successful conversation with the fewest decisions, use GPT4All. If you are building software around a local model, choose Ollama. If you want a desktop model manager and your machine meets its requirements, choose LM Studio.
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Check hardware, memory, and storage first
RAM and VRAM determine what will load
Model parameter count is only a rough guide. Quantization, context length, the runner, and other applications all affect the memory required at runtime. Leave headroom for your operating system and browser rather than matching a model’s minimum exactly.
LM Studio currently recommends:
- Windows: 16 GB of RAM and at least 4 GB of dedicated VRAM.
- macOS: 16 GB or more on Apple Silicon. An 8 GB Mac may work with smaller models and modest context sizes.
- Linux: x64 or ARM64 hardware with Ubuntu 20.04 or newer for its AppImage distribution.
GPT4All is designed for consumer hardware and documents models in the 3–13 billion parameter range. It explicitly supports operation without a GPU, although CPU-only generation is generally slower.
Plan disk space for model files
Model downloads are multi-gigabyte files. GPT4All’s published examples include a 2.18 GB Phi-3 Mini Instruct file, a 4.66 GB Llama 3 Instruct file, and a 7.37 GB GPT4All Snoozy file. Keep additional space for more than one model, temporary downloads, updates, and your operating system.
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- On Windows, determine whether the computer is x64 or ARM. LM Studio supports both; x64 requires AVX2, while ARM support includes Snapdragon X Elite systems.
- On macOS, LM Studio requires an Apple Silicon M1, M2, M3, or M4 chip and macOS 14.0 or newer. Intel Macs are not supported by its current requirements.
- On Linux, LM Studio requires Ubuntu 20.04 or newer; versions newer than Ubuntu 22 are described as not well tested. Its Linux package is an AppImage.
Set up on Windows
Option A: install Ollama
- Confirm your Windows architecture and available RAM, free disk space, and dedicated VRAM.
- Open PowerShell and run the official installer:
irm https://ollama.com/install.ps1 | iex. - After installation, keep model choice conservative until you know how much memory your machine can spare. Ollama’s documentation emphasizes that speed depends on hardware and that large models are slow without a strong GPU.
- When privacy is essential, select a model that runs locally rather than a cloud model. The two modes have different data paths.
Option B: use a graphical application
Download GPT4All or LM Studio for Windows from its official distribution. GPT4All is the lower-friction choice when you do not have a GPU. LM Studio’s published recommendation is 16 GB RAM plus 4 GB dedicated VRAM, so a system below those guidelines should begin with a smaller quantized model.
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Windows first-chat checklist
- Close memory-heavy applications before loading the model.
- Keep the computer connected to power for a long generation or download.
- Start with a model whose stated RAM requirement is comfortably below your installed memory.
- If the model fails to load, reduce parameter count or choose a smaller quantization before changing other settings.
Set up on macOS
Apple Silicon and LM Studio
LM Studio’s current Mac requirements are Apple Silicon (M1, M2, M3, or M4), macOS 14.0 or newer, and 16 GB or more of RAM recommended. Intel Macs are not supported by those requirements. An 8 GB Apple Silicon Mac may still run smaller models with a modest context, but expect tighter limits.
Install Ollama
- Check the Mac chip and memory in Apple menu → About This Mac.
- For a shell-based installation, run the official command:
curl -fsSL https://ollama.com/install.sh | sh. - Alternatively, use the official Mac download for a desktop workflow.
- Choose a local model and avoid cloud integrations when prompts must remain on the Mac.
Use GPT4All on a Mac
Install the Mac application, open it, add a model, and load it from the Chats view. A smaller model is the practical starting point on 8 GB systems; 16 GB provides more room for model weights and context.
Set up on Linux
Ollama shell installation
On a supported Linux distribution, run curl -fsSL https://ollama.com/install.sh | sh. Verify that the installer completed, then choose a model sized for your RAM and any available GPU memory. CPU-only operation is possible, but generation may be slow for larger models.
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LM Studio distributes a Linux AppImage and currently requires Ubuntu 20.04 or newer. Ubuntu releases newer than 22 are described as not well tested. Download the AppImage, make it executable using your desktop environment or file manager, and launch it. If it will not start, check both the Ubuntu version and whether the file has execute permission.
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GPT4All on Linux
Install the Linux build, download a model from its catalog, and use the same chat workflow described below. GPT4All documents no-GPU operation, making it suitable for a CPU-only Linux machine when you accept slower generation.
Download and start your first model with GPT4All
The following sequence follows GPT4All’s documented quickstart and is the most direct path to a first local chat:
- Install GPT4All for Windows, macOS, or Linux and open the application.
- Click Start Chatting.
- Click + Add Model.
- Select a model and download it. Check its file size, stated RAM requirement, and license before confirming.
- Go to Chats.
- Click Load Default Model.
- Send one of the example prompts or type your own question.
GPT4All describes this process as running large language models privately on everyday desktops and laptops. “Private” here means the downloaded local model executes on the device; connecting a remote model API changes where prompts are processed.
Pick model size and quantization deliberately
Use the smallest model that can do the job
A 3–7B model is usually a more forgiving first experiment on consumer hardware than a much larger model. GPT4All’s catalog examples illustrate the range:
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| Example | Published file size | Published RAM requirement | Catalog attribution and date |
|---|---|---|---|
| Phi-3 Mini Instruct | 2.18 GB | 4 GB | GPT4All/Nomic, 2024 |
| Llama 3 Instruct | 4.66 GB | 8 GB | GPT4All/Nomic, 2024 |
| GPT4All Snoozy | 7.37 GB | 16 GB | GPT4All/Nomic, 2024 |
These are catalog examples, not universal performance guarantees. Two models with similar parameter counts can have different memory needs, response quality, and speed.
Understand quantization
Quantization stores weights with fewer bits. GPT4All explains that a smaller quantization, such as 4-bit instead of 16-bit, is much faster and less memory intensive, with a slight performance trade-off. It is a practical way to fit a useful model into consumer RAM:
- Choose a smaller quantization when the full-precision file will not fit or generation is unacceptably slow.
- Use more precision when your hardware has sufficient memory and the task is sensitive to output quality.
- Compare the model’s stated RAM requirement with your free memory, not just total installed RAM.
Check the file format and license
LM Studio’s getting-started material describes model files such as .gguf and .safetensors; GPT4All says many catalog models use .gguf. Download files intended for your runner, and read the model license because permissions differ between personal and commercial use.
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Keep local privacy genuinely local
A local runner can keep prompts on-device, but privacy is a configuration choice rather than a blanket property of the application.
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- Use a downloaded local model and confirm that the interface identifies it as local.
- Do not enable a remote model API when prompts contain confidential material. GPT4All warns that connecting a remote API sends prompts to that provider.
- In Ollama, distinguish local models from cloud models. The official documentation describes local models running on your computer and cloud models running on Ollama’s servers.
- Review extensions, telemetry, document connectors, and update settings according to your organization’s policy.
- Remember that local execution does not automatically protect files saved in chat history, application logs, backups, or screen recordings.
Improve speed without guessing
No authoritative cross-platform tokens-per-second benchmark is established for these runners. Speed varies with model architecture, quantization, context length, CPU and GPU, memory bandwidth, and thermal limits. Test the workload you actually care about.
- Reduce model size first. A smaller parameter count or quantization usually has the largest effect on memory pressure.
- Shorten the context. Long conversation history and large documents increase memory use and can slow generation.
- Close competing applications. Browsers, virtual machines, and creative software can consume the RAM or VRAM the model needs.
- Use suitable GPU acceleration. A compatible GPU can improve generation, but the exact benefit depends on the runner, model, drivers, and how much of the model fits in VRAM.
- Control thermals. Sustained workloads on laptops may slow as the system heats up; keep vents clear and use a stable power source.
Troubleshoot the common failures
The model will not load
- Check the model’s stated RAM requirement against free memory.
- Close memory-heavy programs.
- Select a smaller parameter count or lower-bit quantization.
- Confirm that the file format is supported by the runner and that the download completed without errors.
Generation is too slow
- Switch to a smaller model or reduce context length.
- Check whether the runner is using available GPU hardware.
- On CPU-only systems, expect slower generation, especially with large models. Ollama explicitly warns that large models are slow without a strong GPU.
- Test a short prompt before judging the system on a long document or extended conversation.
LM Studio will not install or launch on macOS
Verify that the Mac uses an M1, M2, M3, or M4 chip and macOS 14.0 or newer. Intel Macs are outside the current support requirements.
LM Studio will not launch on Linux
Confirm Ubuntu 20.04 or newer, note that releases newer than Ubuntu 22 are not well tested, and ensure the AppImage has execute permission.
Windows model performance is unexpectedly poor
Check whether the machine has 16 GB of RAM and at least 4 GB of dedicated VRAM, the current LM Studio recommendations. An integrated GPU or a system with less memory may need a smaller quantized model.
You are worried that prompts are leaving the machine
Disable optional remote APIs and cloud models, then select a downloaded local model. If an application does not clearly identify the processing mode, do not enter sensitive information until you can verify it.
A practical first-session checklist
- Record operating system, CPU architecture, installed RAM, free disk space, and dedicated VRAM.
- Choose GPT4All for the simplest GUI, Ollama for CLI/API integration, or LM Studio when its hardware guidance matches your machine.
- Reserve several gigabytes for the first model and future updates.
- Start with a small quantized model and read its license.
- Run a short prompt locally before importing private documents.
- Observe memory use, generation speed, heat, and whether the model remains responsive during a longer conversation.
- Only after the baseline works, try a larger model, longer context, or additional integrations.
What local LLMs can and cannot promise
Local inference can provide offline access, predictable control over where prompts are processed, and an API or desktop chat without a recurring hosted request. It does not guarantee high speed, expert-level answers, or complete privacy if you enable remote services. Hardware limits the model, model licenses vary, and the same model can feel very different on a CPU-only laptop versus a machine with substantial GPU memory.
There is no universal “best” local model in the available documentation and no cross-platform benchmark that would make one runner fastest for every computer. The reliable approach is to match the runner to your workflow, start with a model that fits comfortably, and measure your own prompts before expanding the setup.
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