How to run Meta’s Llama model locally on Windows, macOS, or Linux

Yes—you can run Meta’s Llama models entirely on a Windows PC, Mac, or Linux computer. The practical choice depends on how much control you want: Ollama is the shortest command-line setup, LM Studio provides a graphical model browser and chat window, and llama.cpp offers the most direct control over GGUF files and local servers. Plan for at least 16GB of system RAM for a comfortable general setup; LM Studio’s 2026 guidance also recommends at least 4GB of dedicated Windows VRAM. Larger models or longer contexts can require more memory and may fall back from GPU to system RAM or CPU.

Choose the local Llama route that fits your workflow

All three routes keep inference on your computer after the model files are downloaded. They differ mainly in setup style, control, and how easily they fit into scripts or local applications.

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Ollama’s documentation summarizes the central trade-off as “Speed depends on the hardware.” LM Studio describes its workflow as “Natively local.” Neither statement is a universal speed guarantee: performance varies with the model, quantization, context length, memory bandwidth, and whether work is handled by a GPU, CPU, or both.

Hardware and operating-system requirements

Local inference is governed by memory capacity first. A model must fit in available GPU memory, system RAM, or a combination that the runtime can use. If it does not fit in dedicated VRAM, a runtime may place part of it in system RAM or run it on the CPU; that usually works more slowly.

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These figures are planning recommendations, not a promise that a particular Llama release will run at a particular speed. A larger model consumes more memory, and increasing the context length also changes the memory required. Start with a model that fits comfortably, then observe memory use and generation speed before moving up to a larger one.

How acceleration works

Meta’s deployment documentation describes llama.cpp as supporting CPU-only, mixed CPU/GPU, and full-GPU execution. The available acceleration layers include Apple Metal, NVIDIA CUDA, AMD ROCm, and Vulkan, depending on the operating system, hardware, and build. Ollama and LM Studio may select an appropriate backend automatically, while llama.cpp exposes more of the underlying choices.

There is no single reliable “Llama runs at X tokens per second” figure for Windows, macOS, and Linux. Treat any speed estimate as hardware- and model-specific, and measure the exact model and context you intend to use.

Install Llama on Windows

For most Windows users, start with Ollama or LM Studio. Choose llama.cpp when you need direct checkpoint and server control or want to integrate the runtime into a development workflow.

Option 1: Ollama on Windows

1. Download and install the current Windows release of Ollama.
2. Open PowerShell, Windows Terminal, or Command Prompt after installation.
3. Run the Llama model tag shown in the current Ollama catalog, for example ollama run <current-llama-tag>. Keep the tag as a variable in your notes because catalog names and versions change.
4. When the model starts, enter a short prompt and confirm that you receive a response without using a hosted chat service.
5. For a local-only test, disable networking temporarily before sending the prompt. The model should still answer if its files are already downloaded and the Ollama service is running locally.

Do not copy an old model tag blindly from a tutorial. Look up the current Llama entry in Ollama’s catalog at the time you install, then record the tag and date with your setup notes.

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Option 2: LM Studio on Windows

1. Install the current Windows build of LM Studio. On an x64 computer, confirm that the processor supports AVX2; Windows ARM systems should use the supported ARM path.
2. Open LM Studio and use its model browser to locate a Llama model.
3. Download a model whose file size and expected runtime memory fit your available RAM and VRAM. The application’s hardware guidance is a starting point, not a guarantee for every model.
4. Open the downloaded model in the chat interface and send a short prompt.
5. If an application needs a local API, open LM Studio’s server controls and start its OpenAI-compatible local endpoint. Use the endpoint details shown by the current application rather than hard-coding details from an old guide.

This route avoids beginning in a shell: the browser, download controls, chat window, and server controls are all in one application.

Option 3: llama.cpp on Windows

1. Install the llama.app Windows desktop application or follow the current Windows build instructions for llama.cpp.
2. Obtain a Llama checkpoint in GGUF format that fits your available memory.
3. Open a terminal and verify the installation with llama cli --version.
4. Use the installed tools to load the GGUF model for interactive use, or start the local service with llama serve. Check the command’s current help output for the model-file and backend options supplied by your build.

llama.cpp is the most suitable Windows route when you need to choose the exact checkpoint file, decide how much work is offloaded to the GPU, or expose a local service for another program.

Install Llama on macOS

Apple Silicon Macs are the current supported target for LM Studio, which lists macOS 14 or newer in its requirements. Ollama provides a macOS download, and llama.app provides a macOS desktop installer. On supported Macs, Metal can provide hardware acceleration for llama.cpp workloads.

Ollama on macOS

1. Install the current macOS version of Ollama.
2. Open Terminal.
3. Run the Llama tag currently listed in the Ollama catalog: ollama run <current-llama-tag>.
4. Send a short prompt and watch memory use while it generates.
5. If you require a strictly local test, turn off networking after the model download and run the prompt again. A locally stored model should not need a cloud connection to generate text.

LM Studio on Apple Silicon

1. Confirm that the Mac uses Apple Silicon and runs macOS 14 or newer.
2. Install LM Studio and open its model browser.
3. Download a Llama model that fits the Mac’s unified memory. Start with a smaller file if the available memory is limited.
4. Load the model in the chat window and test with a short prompt.
5. Use the server controls if you need the OpenAI-compatible local endpoint for a development tool or script.

Apple’s unified-memory design means the same pool is shared by the operating system, applications, and model workload. Leave headroom rather than selecting a model that consumes nearly all available memory.

llama.cpp on macOS

1. Install the llama.app desktop application for Mac.
2. Download a compatible GGUF Llama checkpoint.
3. Verify the command-line installation with llama cli --version if the CLI is included in your installation.
4. Use the llama tools to run the checkpoint or start llama serve for a local service.

When the build supports Metal, llama.cpp can use the Mac GPU path; it can also run on the CPU. The selected backend and the model’s memory footprint determine the practical speed.

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Install Llama on Linux

Linux users can choose an Ollama installer, LM Studio’s Linux package or AppImage path, or the llama.cpp one-line installer. LM Studio currently lists Ubuntu 20.04 or newer for its Linux support.

Ollama on Linux

1. Run the current Linux installer supplied by Ollama.
2. Open a new terminal so the installed command and service are available.
3. Run the current Llama catalog tag with ollama run <current-llama-tag>.
4. Test a short prompt, then monitor memory and generation speed before selecting a larger model.

If the command is not found immediately after installation, open a new shell session and check that the installer’s binary location is on your PATH.

LM Studio on Linux

1. Confirm that the distribution meets the current requirement; LM Studio lists Ubuntu 20.04 or newer.
2. Install the Linux package or AppImage supplied by LM Studio.
3. Open the model browser, download a Llama model, and load it in the chat window.
4. Start the OpenAI-compatible local endpoint from the server controls when another local application needs API access.

llama.cpp on Linux

1. Run the one-line installer supplied by llama.app:

curl -LsSf https://llama.app/install.sh | sh

2. Verify the installation:

llama cli --version

3. Use a GGUF Llama checkpoint with the installed CLI, or start the local service with llama serve. Consult the help output from your installed version for the current model-file and backend flags.

Linux builds can use CPU, mixed CPU/GPU, or full GPU execution through supported CUDA, ROCm, Vulkan, or other configured backends. The exact choice depends on how your runtime was installed and which hardware drivers are available.

Pick a model without running out of memory

Start with the available memory, not the model’s reputation

Choose a model file that leaves room for the operating system and the application itself. A computer with 16GB of RAM may run a smaller Llama configuration comfortably while struggling with a larger one or a long context. The 16GB recommendation from LM Studio is therefore a baseline for planning, not a universal minimum for every Llama variant.

Understand GPU fallback

If the model cannot fit in dedicated VRAM, the runtime may split the workload between GPU and system RAM or use the CPU. That can let the model run, but generation may become noticeably slower. A smaller model that stays within the available VRAM can be more responsive than a larger model that constantly transfers data between memory pools.

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Measure a short, representative prompt

1. Start with a short prompt that does not require a long conversation history.
2. Watch system RAM, dedicated VRAM, and application-reported load while the response is generated.
3. Repeat with the context length and prompt style you actually expect to use.
4. Only then test a larger model or longer context.

This process gives you a useful result for your own machine without pretending that one cross-platform benchmark applies to every Llama release.

Keep the installation genuinely local

“Local” means the model inference runs on your device; it does not necessarily mean the initial installer or model download can happen without internet access. Download the runtime and model first, then disable networking to test the inference path.

• Install the runtime and download the model while connected.
• Disable Wi-Fi and wired networking, or apply a firewall rule, before the offline test.
• Launch the already installed runtime and submit a short prompt.
• Confirm that the response appears while the computer remains disconnected.
• Re-enable networking only when you need updates or additional model files.

Ollama and llama.cpp are convenient for scripts and local services. LM Studio’s OpenAI-compatible endpoint is useful for interactive testing and for applications that already speak an OpenAI-style API. Keep the endpoint bound and accessible only as broadly as your local workflow requires.

Troubleshoot the common failure modes

The installer runs, but the model will not load

Check the model’s memory requirements against both available RAM and dedicated VRAM. Close other memory-heavy applications, choose a smaller model, or use a runtime mode that permits CPU or mixed CPU/GPU execution. A model that barely fits may still fail when the context grows.

Generation is unexpectedly slow

First determine whether the runtime is using the intended acceleration backend. A CPU fallback, insufficient VRAM, or a model that is too large for the GPU can all reduce speed. Test a smaller model and a short prompt; if that improves responsiveness, the original workload exceeded the machine’s comfortable memory or compute budget.

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Windows reports a compatibility problem

For the LM Studio x64 path, confirm AVX2 support. On a Windows ARM system, install the ARM-supported build rather than assuming the x64 package is appropriate. If you need a different execution path, Ollama or the llama.app Windows application may be a better fit for the hardware and operating-system combination.

The command is not found after installation

Open a new terminal session so environment changes are reloaded, then verify the runtime’s version. For llama.cpp, use llama cli --version. For Ollama, confirm that the installed application or service is running before issuing ollama run.

A local application cannot reach the model server

Confirm that the runtime’s server is running, use the endpoint address and port shown by the current application, and check local firewall rules. LM Studio exposes its endpoint through its server controls; llama.cpp provides llama serve. Do not assume a server is available merely because the chat interface can generate text.

Maintain a setup that survives catalog and version changes

Model tags, installers, and graphical labels change. Record the runtime name, version, operating system, model tag or GGUF filename, and installation date. When you repeat the setup later, check the current catalog and requirements rather than copying an old command unchanged.

• Keep the installer version and model identifier together in your notes.
• Retest after upgrading the runtime or graphics driver.
• Preserve model files that you know work before deleting older versions.
• Recheck memory use after changing the model, quantization, or context length.
• Do not compare speed claims across machines unless the model, context, backend, and hardware are equivalent.

Which route should you use?

Choose Ollama when setup time matters most

Ollama is the shortest path from installation to a running Llama prompt. Its terminal workflow is easy to script, but you must use the current catalog tag and accept that the catalog can change.

Choose LM Studio when you want visual controls

LM Studio is the most approachable option for browsing models, downloading them, chatting, and starting a local OpenAI-compatible endpoint without beginning in a shell. Follow its current platform requirements, especially the Apple Silicon and macOS 14 requirement for Mac and the Ubuntu 20.04-or-newer guidance for Linux.

Choose llama.cpp when control and portability matter

llama.cpp is the direct route to GGUF checkpoints, backend selection, and a local server. It suits developers who need repeatable files and commands, or who want to decide explicitly between CPU, mixed CPU/GPU, and full GPU execution.

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A practical first-day checklist

• Pick one runtime rather than installing all three at once.
• Confirm the operating-system and processor requirements.
• Ensure at least 16GB of RAM is available if you want to follow LM Studio’s general recommendation, and check dedicated VRAM on Windows.
• Download a model that fits the machine’s memory with headroom.
• Verify the runtime version or application build.
• Run a short prompt and observe memory use and speed.
• Repeat the prompt with networking disabled if local-only operation is required.
• Record the model identifier and installation date so you can reproduce the setup when catalogs change.

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Linuxllama.cppLM Studiolocal AImacOSMeta LlamaOllamaWindows