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Your computer determines which models are practical. A model file can occupy several gigabytes, while RAM, dedicated VRAM, processor architecture, context length, and cooling determine whether it loads and how quickly it generates text. The steps below cover Windows, macOS, and Linux, including offline privacy, model sizing, and recovery when a model will not load.
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What running an LLM locally actually means
Local inference means the model weights are downloaded to your computer and the generation process runs there. After the model is installed, a network connection is not required for the basic chat itself. GPT4All describes this as running language models privately on everyday desktops and laptops, and its documentation says that no API calls or GPU are required to get started.
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A local setup has three separate parts:
- Runner: the desktop application, command-line program, or local service that loads model files and performs inference.
- Model: the downloaded weight file, commonly distributed in GGUF format for llama.cpp-compatible runtimes. LM Studio documentation also discusses GGUF and safetensors files.
- Hardware: system RAM, optional dedicated VRAM, CPU instructions, storage, and memory bandwidth. These set the upper limit on model size and practical speed.
Local does not automatically mean every feature is offline. A runner may offer optional remote APIs or cloud models. Those modes send prompts to a provider, so select a downloaded local model when on-device processing is a requirement.
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Choose a runner before downloading a model
Pick the interface that matches the work you want to do. Installing several runners is possible, but each can maintain its own model files and consume additional storage.
| 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 supports operation without a GPU. | The available catalog and performance depend on supported models and your hardware. |
| Developer CLI or API | Ollama | Official installers cover macOS, Linux, and Windows, with a documented local API path for scripts and applications. | Large models can be slow without a strong GPU; its cloud mode is separate from local execution. |
| GUI with explicit requirements | LM Studio | Runs on supported Mac, Windows, and Linux systems and publishes CPU, RAM, GPU, and operating-system requirements. | Current Mac support is Apple Silicon only, and older or unsupported systems cannot use the application. |
| Maximum manual control | A direct llama.cpp-compatible workflow | You choose the runtime, model file, quantization, and launch settings yourself. | More command-line and compatibility decisions are required. |
If you simply want a first successful local conversation, start with GPT4All. If you expect to call the model from software, start with Ollama. Choose LM Studio when its hardware checklist matches your machine and you prefer a visual model manager.
Check hardware, operating system, and storage first
LM Studio requirements by platform
| Platform | Documented requirements | Practical implication |
|---|---|---|
| macOS | Apple Silicon M1, M2, M3, or M4; macOS 14.0 or newer; 16GB or more RAM recommended. Eight gigabytes may work with smaller models and modest context sizes. Intel Macs are not supported. | Check the chip and macOS version before downloading. An Intel Mac needs a different runner. |
| Windows | x64 or ARM, including Snapdragon X Elite; x64 requires AVX2; 16GB RAM recommended; at least 4GB dedicated VRAM recommended. | Confirm whether Windows is x64 or ARM and whether the processor exposes AVX2. Integrated graphics may leave you CPU-bound. |
| Linux | x64 or ARM64; AppImage distribution; Ubuntu 20.04 or newer. Versions newer than Ubuntu 22 are described as not well tested. | Verify the distribution and make the AppImage executable before launching. |
These are LM Studio’s current published requirements, not a universal minimum for every runner. GPT4All documents no-GPU operation, so a computer that cannot meet LM Studio’s recommendations may still run a smaller model elsewhere.
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Memory and disk planning
Reserve storage before downloading. Published GPT4All examples include a 2.18GB Phi-3 Mini Instruct file, a 4.66GB Llama 3 Instruct file, and a 7.37GB GPT4All Snoozy file. The model file is not the only use of disk space: leave room for the runner, operating-system free space, updates, and any second model you want to keep.
RAM must hold the model’s working data as well as the operating system and your conversation context. A model’s stated RAM requirement is a useful admission check, not a speed guarantee. Close browsers, games, virtual machines, and other memory-heavy applications before loading a model if you are close to the limit.
Prepare a safe first run
- Record your operating system version, processor architecture, installed RAM, and available disk space.
- If you plan to use LM Studio on Windows, check for AVX2 on x64 and note whether you have at least 4GB of dedicated VRAM.
- Choose a model in the 3–13B range for consumer hardware, which is the range GPT4All identifies as its optimization target.
- Prefer a quantized download, commonly 4-bit, when memory is limited. Keep the model’s published RAM requirement and license information with your notes.
- Download only from the runner’s model catalog or a source you trust, and read the model license before personal or commercial use. GPT4All notes that licenses vary.
Windows: install a runner and start chatting
Option A: Ollama for a CLI or local service
- Open PowerShell with an account permitted to install software.
- Run Ollama’s documented installer command:
irm https://ollama.com/install.ps1 | iex. If your organization’s policy blocks script execution, use the official Windows download instead of bypassing that policy. - After installation, launch the Ollama application or service and obtain a model through its local workflow. Keep local models distinct from any cloud option shown by the application.
- Start with a small quantized model and send a short test prompt. Increase model size only after confirming that the first model loads reliably.
Ollama states that speed depends on hardware and warns that large models are slow without a strong GPU. Treat the first response as a compatibility check rather than a benchmark; generation time varies with model architecture, quantization, context length, CPU or GPU, memory bandwidth, and thermals.
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Option B: GPT4All for a point-and-click setup
- Download and install GPT4All for Windows, then open it.
- Select Start Chatting.
- Choose + Add Model and download a model whose file size and RAM requirement fit your computer.
- Open Chats and select Load Default Model.
- Try one of the example prompts or enter your own question.
This path does not require a GPU according to GPT4All’s documentation. If the first model cannot load, do not repeatedly retry the same file; move to a smaller parameter count or a smaller quantization.
Option C: LM Studio
Install the Windows build only after confirming x64 or ARM compatibility. On x64, AVX2 is required. LM Studio recommends 16GB of RAM and at least 4GB of dedicated VRAM; systems below those recommendations should use a smaller model and expect more CPU work. In the application, download a compatible model, load it, and open a chat. Keep enough free disk space for the full model file rather than relying on the displayed parameter count alone.
macOS: choose the path that matches your Mac
Apple Silicon Macs with LM Studio
LM Studio’s current macOS support requires an M1, M2, M3, or M4 chip and macOS 14.0 or newer. Intel Macs are not supported by LM Studio. Its recommendation is 16GB or more RAM; an 8GB Mac may work with a smaller model and modest context size, but it leaves less room for multitasking.
- Open About This Mac and verify the chip generation and macOS version.
- Install the compatible LM Studio release.
- Download a small, quantized model, load it, and test a short prompt before choosing a larger file.
Ollama on macOS
- Open Terminal.
- Run the documented installer:
curl -fsSL https://ollama.com/install.sh | sh. - Launch the installed application or local service and select a downloaded local model.
- Test with a short prompt, then monitor memory pressure while increasing context or model size.
Ollama also provides an official Mac download for a desktop workflow. A Mac can run a local model without a discrete graphics card, but speed is still determined by the chip, unified memory, model size, quantization, context, and thermal limits.
GPT4All on Mac
Install GPT4All for macOS, open it, select Start Chatting, use + Add Model to download a compatible model, then go to Chats and choose Load Default Model. On an 8GB system, select a smaller model and avoid a large context window. The same local-versus-remote privacy distinction applies: a downloaded local model stays on the Mac, while a connected remote API sends prompts outside it.
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Linux: account for AppImages and distribution support
Ollama installation
On a supported Linux machine, open a shell and run Ollama’s documented command:
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curl -fsSL https://ollama.com/install.sh | shStart the installed local service, obtain a small model, and test it before allocating space to larger files. CPU-only systems can work, but generation is normally slower than a configuration with suitable GPU acceleration.
LM Studio on Linux
LM Studio is distributed as an AppImage and currently requires Ubuntu 20.04 or newer. Its documentation says releases newer than Ubuntu 22 are not well tested. Download the AppImage, grant execute permission using your desktop file manager or distribution tools, and launch it. If it does not open, verify both the Ubuntu version and AppImage execution permissions before changing model settings.
GPT4All on Linux
Install the Linux build, open the application, and follow the documented sequence: Start Chatting, + Add Model, download a model, open Chats, and select Load Default Model. GPT4All’s no-GPU support makes it a practical first test on a CPU-only Linux workstation.
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Use parameter count as a starting filter
GPT4All is optimized for 3–13B parameter models on consumer hardware. Parameter count is not the same as file size or required RAM, because architecture, quantization, context, and runtime overhead also matter. The published examples below show why checking the model card is essential.
| Model example | Published file size | Published RAM requirement | Publisher and year |
|---|---|---|---|
| Phi-3 Mini Instruct | 2.18GB | 4GB | GPT4All/Nomic, 2024 |
| Llama 3 Instruct | 4.66GB | 8GB | GPT4All/Nomic, 2024 |
| GPT4All Snoozy | 7.37GB | 16GB | GPT4All/Nomic, 2024 |
These figures describe the listed model files and requirements, not guaranteed response speed. Keep additional RAM available for the operating system and the conversation context.
Why quantization matters
Quantization stores weights with fewer bits. A 4-bit version therefore uses substantially less memory than a 16-bit version and generally runs faster on consumer hardware, at the cost of some quality. GPT4All specifically describes smaller quantization as faster and less memory intensive with slightly worse performance. For a first local chat, a well-supported 4-bit file is often a more useful choice than an unquantized file that cannot load.
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GGUF and safetensors
Many GPT4All catalog entries use GGUF, a common format for llama.cpp-compatible runtimes. LM Studio’s getting-started material also references GGUF and safetensors. A file must match the runner and model architecture; a large file with the wrong format will not become usable simply because there is enough disk space.
Privacy: verify where prompts are processed
With a downloaded local model, prompts can remain on the device. GPT4All says its local models are downloaded and run locally and privately. That statement does not cover optional remote connections: GPT4All warns that connecting a remote model API sends prompts to the provider.
Ollama makes the same distinction between local models running on your computer and cloud models running on its servers. Before entering confidential material, inspect the selected model, connection setting, and any integration that offers a hosted provider. If the requirement is strict offline processing, disable or avoid remote APIs and cloud modes and test with networking unavailable after the model has downloaded.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What controls speed and responsiveness
There is no universal tokens-per-second figure for these runners. The published documentation supplies requirements and example file sizes, not a cross-platform benchmark. Your result depends on:
- Model architecture and parameter count.
- Quantization level and whether the runtime can accelerate it.
- Prompt and context length; longer histories require more memory and computation.
- CPU instruction support, GPU availability, VRAM, and memory bandwidth.
- Thermal throttling during sustained generation.
For a faster first result, reduce model size and context before changing several variables at once. A smaller model that answers promptly is easier to evaluate than a larger model that repeatedly exhausts memory.
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The model will not load
- Check the model’s stated RAM requirement against available memory, not total installed memory.
- Close memory-heavy applications and retry.
- Choose a smaller parameter count or a lower-bit quantization.
- Confirm that the file format and architecture are supported by the selected runner.
Generation is unacceptably slow
- Use a smaller model or reduce the context length.
- Check whether the runner is using an available, suitable GPU; otherwise expect CPU generation.
- On Ollama, remember its explicit warning that large models are slow without a strong GPU.
- Watch for thermal throttling during long sessions.
LM Studio will not install on a Mac
Verify that the Mac uses Apple Silicon M1–M4 and macOS 14.0 or newer. Intel Macs are currently unsupported by LM Studio, so use another runner instead of trying to force the installer.
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LM Studio will not launch on Linux
Confirm Ubuntu 20.04 or newer, note that versions newer than Ubuntu 22 are not well tested, and verify that the downloaded AppImage has execute permission.
You are worried that data is leaving the device
Switch from a remote API or cloud model to a downloaded local model. Check the active connection in the runner before submitting sensitive prompts; local installation alone does not disable optional hosted integrations.
A practical decision framework
Choose GPT4All when simplicity matters most
Use GPT4All when you want the shortest path from installation to a private desktop chat and do not have a dedicated GPU. Its documented sequence is explicit, and its model examples make storage and RAM planning straightforward.
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Use Ollama when you need a command-line-oriented workflow or a local API for an application. Install it with the platform command, keep cloud and local modes separate, and size the first model conservatively because large models can be slow without strong graphics hardware.
Choose LM Studio when hardware visibility matters
Use LM Studio when you want a graphical workflow and your system meets its published requirements. It is especially useful when you need to check Apple Silicon, AVX2, RAM, VRAM, and Ubuntu compatibility before committing to a model download.
Choose a direct llama.cpp workflow when control outweighs convenience
A direct workflow gives you more control over GGUF files, quantization, runtime flags, and hardware layers. It also places responsibility for matching the model, runtime, and command-line settings on you, so it is better suited to users comfortable diagnosing compatibility issues.
First-session checklist
- The runner opens on your operating system and architecture.
- You have more free disk space than the model file size, with room for the application and another model if needed.
- The model’s RAM requirement fits after closing unnecessary applications.
- The downloaded format is supported by the runner.
- You selected a local model rather than a cloud or remote API option.
- A short prompt completes before you increase context length or model size.
- You have checked the model license for your intended personal or commercial use.
Start with the smallest model that answers your test prompt reliably, then change one variable at a time. That approach gives you a working baseline and makes later upgrades—more RAM, a suitable GPU, a larger SSD, or a larger model—measurable rather than speculative.
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