“Hardware-agnostic models in vLLM” means models may be served across multiple hardware backends—not that every model, feature, or configuration runs unchanged on every device. vLLM documents paths for NVIDIA, AMD, Intel, Apple Silicon, and several CPU platforms, but compatibility depends on the backend, model architecture, software versions, data type, and requested features.
Which hardware platforms does vLLM support?
The vLLM 0.31.0 installation guide lists these platform paths. The versioned installation guide is the reference for that release; installation requirements can change between versions.
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| Platform | Documented path |
|---|---|
| GPU | NVIDIA CUDA, AMD ROCm, Intel XPU, and Apple Silicon through vLLM-Metal |
| CPU | Intel/AMD x86, ARM AArch64, Apple silicon, and IBM Z (S390X) |
| Third-party hardware | Plugins outside the main vLLM repository that follow the Hardware-Pluggable RFC |
A platform appearing on this list is not a guarantee that every model or feature works on it. Third-party plugins are also distinct from platform paths maintained in the main project; check the plugin’s own supported hardware, features, and maturity.
What determines whether a model will run?
Compatibility is a combination of the model and the deployment configuration, not just the device vendor. Check each of these for the exact vLLM release you plan to use:
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- Device and architecture: Confirm the precise GPU family or CPU architecture is supported, rather than relying on a broad label such as “AMD GPU.”
- Software environment: Match the operating system, driver, runtime, Python version, and package or wheel to the platform guide. Installation routes can differ by backend.
- Model and features: Verify the model architecture and every requested feature on the target backend. The platform list alone does not establish universal model or feature support.
- Precision and memory: Confirm the required data type or quantization path works on that backend and that the device has sufficient memory for the model and workload.
- Maintenance path: Determine whether support comes from the main vLLM project, a platform-specific build, or a separately maintained plugin.
The rolling GPU installation guide and rolling CPU installation guide describe platform-specific prerequisites and limitations. Because these pages are rolling documentation, recheck them for the release you are installing.
What do the GPU requirements say?
The rolling GPU guide gives examples of requirements that vary by hardware path. NVIDIA GPUs are listed with compute capability 7.5 or higher. AMD support is tied to specific GPU families and ROCm minimums; Intel’s path names Data Center and ARC GPUs and requires the vllm-xpu-kernels dependency. Apple Silicon is supported through Metal. Use the guide to confirm the exact device and software requirements rather than treating these examples as interchangeable installations.
The guide also states that native Windows is unsupported and describes Windows Subsystem for Linux (WSL) as an option. Check the current installation instructions for the supported WSL setup and dependencies.
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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →What is different about CPU support?
The CPU guide describes basic inference on x86 and Arm, experimental Apple Silicon CPU support, and experimental IBM Z support. “Experimental” is a meaningful qualification: it does not imply the same maturity or feature coverage as a general support guarantee.
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AMD Zen data-type limitation
For AMD Zen, the guide says float16 is unsupported on ZenCpuPlatform; bfloat16 and float32 are supported. A model declared with float16 is downcast at load time. This is a Zen-specific behavior, not a limitation to apply to every CPU backend.
How should you compare two hardware options?
Use the same target model and workload to assess both options. Compare the requirements first, then test performance under comparable conditions:
- Identify the exact accelerator or CPU generation and check that it is named or otherwise covered by the installation path for your vLLM release.
- Confirm the required operating system, driver, runtime, Python version, and package or wheel combination.
- Check that the model architecture and requested features are supported on both backends.
- Verify the data type or quantization route and available memory for each configuration.
- Record whether each installation uses the main project, a platform-specific build, or a third-party plugin.
- Benchmark your workload with the same model, precision, batch size or concurrency, and context settings. Report results only for those tested conditions.
The official installation guides describe platform and installation differences; they do not provide a general cross-platform performance ranking. A platform list is not evidence that one backend is faster than another.
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsWhat “hardware-agnostic” does—and does not—promise
The phrase is useful shorthand for vLLM’s presence across multiple hardware backends. It should not be read as “write once, run everywhere” for every model and feature. A defensible portability claim names the vLLM version, hardware, installation path, model configuration, and any feature or precision limits that matter.
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