- Built for AI & Analytics – Optimized for large-scale data pipelines
- High Performance – Ideal for demanding storage workloads.

This README provides instructions for building MinIO from source and deploying onto baremetal hardware.
Use the [MinIO Documentation](https://github.com/minio/docs) project to build and host a local copy of the documentation.

## MinIO is Open Source Software

    - [**Node**](#node-3)
      - [**Pod API**](#pod-api)
      - [**Hardware Accelerators**](#hardware-accelerators)
      - [**Container Runtime**](#container-runtime)
      - [**Kubelet**](#kubelet)
        - [**Other**](#other-5)
    - [**OpenStack**](#openstack-2)
    - [**Scheduling**](#scheduling-2)
      - [**Hardware Accelerators**](#hardware-accelerators-1)
      - [**Other**](#other-6)
    - [**Storage**](#storage-3)

  
  VM Hardware version less than 15 is deprecated as of v1.21. Please consider upgrading the Node VM Hardware version to 15 or above. vSphere CSI Driver recommends Node VM's Hardware version set to at least vmx-15.
  

is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads. This README provides quickstart instructions on running MinIO on bare metal hardware, including container-based installations. For Kubernetes environments, use the [MinIO Kubernetes Operator](https://github.com/minio/operator/blob/master/README.md). ## Container Installation Use the following commands to run a standalone MinIO server...

    * Fix stop hook failure on kubernetes-worker charm

    * Fix handling of juju kubernetes-worker.restart-needed state

    * Fix nagios checks in charms

  * Enable GPU mode if GPU hardware detected ([#43467](https://github.com/kubernetes/kubernetes/pull/43467), [@tvansteenburgh](https://github.com/tvansteenburgh))

interactions between pods and host resources, and manage the lifecycle
of pods scheduled on a node.

For the 1.8 release, SIG Node continued to focus
on a broad set of workload types, including hardware and performance
sensitive workloads such as data analytics and deep learning. The SIG also
delivered incremental improvements to node reliability.

[SIG Node]: https://github.com/kubernetes/community/tree/master/sig-node

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    <_comment>Windows setup INFormation</_comment>
    <tika:link>http://msdn.microsoft.com/en-us/library/windows/hardware/ff549520(v=vs.85).aspx</tika:link>
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    <magic priority="30">

* `tf.lite`
    * LiteRT announced a [new release](https://developers.googleblog.com/en/litert-maximum-performance-simplified/) at Google IO ‘25 that improves upon TFLite, particularly in terms of NPU and GPU hardware acceleration and performance for on-device ML and AI applications. The APIs are available in Kotlin and C++.

impl.Register("aes", "POWER8", &supportsAES) } } // checkGenericIsExpect is called by the variable-time implementation to make // sure it is not used when hardware support is available. It shouldn't happen, // but this way it's more evidently correct. func checkGenericIsExpect() { if supportsAES { panic("crypto/aes: internal error: using generic implementation despite hardware support") } } type block struct { blockExpanded } func newBlock(c *Block, key []byte) *Block { switch len(key) { case aes128KeySize: c.rounds...

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