.. meta::
   :description: Learn how to partition AMD GPUs/APUs.
   :keywords: AMD, GPU, APU, partitioning, ROCm, MI300X, MI300A

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AMD GPU/APU Partitioning
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Partitioning Overview
^^^^^^^^^^^^^^^^^^^^^^

Modern large-scale AI and HPC workloads demand fine-grained control over GPU resource allocation, memory isolation, and multi-tenant scheduling. AMD's Instinct™ MI300 series GPUs — including the MI300X GPU and MI300A APU — support flexible partitioning schemes that allow users to logically subdivide a single device into multiple independent partitions optimized for different workloads.

This documentation portal serves as a centralized index for navigating the complete GPU partitioning workflow on AMD platforms. It links to detailed technical guides for each supported GPU, including:

- **Architecture deep dives** to understand partitioning capabilities.
- **Quick start instructions** to apply compute and memory partition modes using `amd-smi`.
- **Guide to run vLLM workload** for inference benchmarking.
- **Troubleshooting resources** for resolving partitioning issues in production environments.

Compatibility Matrix
^^^^^^^^^^^^^^^^^^^^^^

To streamline deployment planning and reduce configuration friction, we include below a **GPU Partitioning Schemes Compatibility Matrix**. This matrix outlines which combinations of **Compute Partitioning Modes** (e.g., SPX, CPX) and **Memory Partitioning Modes** (e.g., NPS1, NPS4) are validated for each supported device. It also notes any **minimum amdgpu driver version requirements** necessary to enable specific configurations.

.. important::
   **New to partitioning modes?** Before using the compatibility matrix, it's essential to understand the core concepts of **Compute Partitioning Modes** (SPX, DPX, CPX) and **Memory Partitioning Modes** (NPS1, NPS2, NPS4). These modes determine how compute and memory resources are logically divided across a single device.

   See our detailed overview here: 
    - :ref:`MI300X Compute Partitioning <mi300x_compute-partitioning>` / :ref:`MI300A Compute Partitioning <mi300a_compute-partitioning>`
    - :ref:`MI300X Memory Partitioning <mi300x_memory-partitioning>` / :ref:`MI300A Memory Partitioning <mi300a_memory-partitioning>`

By consolidating this matrix on the index page, users can quickly evaluate platform capabilities and navigate to device-specific documentation with full awareness of what is supported on their hardware and software stack.

.. list-table:: GPU Partitioning Schemes Compatibility Matrix
  :header-rows: 1
  :widths: 20 20 20 20

  * - Instinct GPUs
    - SPX + NPS1
    - DPX + NPS2
    - CPX + NPS4
  * - MI300X
    - ✅
    - ✅
  * - MI300A
    - ✅
    - NA
    - NA
  * - MI325
    - ✅
    - NA
    - NA
  * - MI350
    - ✅
    - ✅
    - NA
  * - MI355
    - ✅
    - ✅
    - NA

.. note::
    DPX + NPS2 bare-metal partitioning is supported on MI300X, MI350, and MI355, but NOT on MI300A or MI325.

Device Documentation
^^^^^^^^^^^^^^^^^^^^^

- :doc:`AMD Instinct MI300X GPU <mi300x/index>` — GPU-specific partitioning for AI inference and HPC workloads. Supports SPX + NPS1, DPX + NPS2, and CPX + NPS4.
- :doc:`AMD Instinct MI300A APU <mi300a/index>` — APU-specific partitioning with integrated CPU and GPU. Supports SPX + NPS1 only.

GPU virtualization
^^^^^^^^^^^^^^^^^^

For GPU passthrough on VMware ESXi (as opposed to bare-metal partitioning), see :doc:`AMD Instinct MI350P on VMware ESXi <../virtualization/esxi/mi350p/index>`.

Start with this index page to assess compatibility, then follow device-specific documentation to implement and validate GPU partitioning configurations in your cluster or platform.