> ## Documentation Index
> Fetch the complete documentation index at: https://docs.magnitude.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Hardware

> Understand what your machine can run and what affects performance.

Magnitude assesses your machine and recommends models that fit. Start with those recommendations rather than choosing a model by parameter count alone.

## Supported configurations

| Machine | Inference support |
| - | - |
| Apple Silicon Mac | Metal GPU acceleration |
| Intel Mac | CPU |
| Windows x64 | CPU, supported NVIDIA CUDA GPUs, or supported Vulkan GPUs |
| Linux x64 or ARM64 | CPU, supported NVIDIA CUDA GPUs, or supported Vulkan GPUs |

See [macOS](/installation/macos), [Windows](/installation/windows), and [Linux](/installation/linux) for OS requirements and installers.

## Apple Silicon

Apple Silicon shares memory between the CPU and GPU. That gives the GPU access to a larger memory pool than a typical laptop's dedicated graphics memory, but macOS and other apps use the same pool.

For example, a Mac with 32 GB of unified memory does not have all 32 GB available for a model. Use Discover's fit assessment and close memory-heavy apps if loading fails.

Metal support is supplied by macOS; you do not need a separate GPU toolkit.

## NVIDIA GPUs

On Linux and Windows, Magnitude can use its CUDA backend with a compatible NVIDIA GPU and driver. The current CUDA builds target Ampere-class and newer GPUs; older CUDA-capable cards are not automatically supported.

Install your GPU's supported NVIDIA driver. You can check that the driver sees it with:

```sh theme={"theme":{"light":"github-light","dark":"github-dark"}}
nvidia-smi
```

Magnitude acquires compatible runtime components; you do not need the CUDA development toolkit. If acceleration is unavailable, check the hardware and compatibility information in the app before downloading a large model.

## AMD and other Vulkan GPUs

Linux and Windows GPU support uses Vulkan for compatible devices. Magnitude does **not** currently ship a ROCm backend.

Use your operating system's supported graphics driver and Vulkan runtime. The backend requires Vulkan 1.1 or later, but the version alone does not guarantee that a particular GPU supports every model. Check the model's compatibility in Catalog.

## CPU-only machines

Magnitude can run models without a supported GPU, including on Intel Macs and Windows machines without a compatible GPU. Smaller models and lower-bit quantizations are usually more practical when response speed matters.

CPU inference uses system RAM. Having enough memory to fit a large model does not mean it will generate quickly; use the speed estimates when choosing.

## Memory and speed trade-offs

* **More memory** lets you consider larger models, higher-fidelity quantizations, or larger contexts.
* **GPU memory** matters on dedicated-GPU systems. A machine with 64 GB of system RAM and an 8 GB GPU does not have 72 GB of interchangeable GPU memory.
* **Longer context** increases memory demand and can slow responses.
* **Other applications** reduce available memory and can affect speed after assessment.

For a faster starting point, move Discover's slider toward **Faster**. For a model that needs less memory, compare the Memory values and quantized variants. See [Models](/models).


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.