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

# Overview

> Fully managed compute clusters for multi-node training and AI inference.

export const TensorFlowTooltip = () => {
  return <Tooltip headline="TensorFlow" tip="An open-source machine learning framework developed by Google for building and deploying neural networks at scale, widely used for both research and production ML applications." cta="Read the TensorFlow documentation" href="https://www.tensorflow.org/">TensorFlow</Tooltip>;
};

export const SlurmTooltip = () => {
  return <Tooltip headline="Slurm" tip="An open-source job scheduler for high-performance computing that provides job management, scheduling, and resource allocation across multiple nodes." cta="Learn more about Slurm on Runpod" href="/instant-clusters/slurm">Slurm</Tooltip>;
};

export const InferenceTooltip = () => {
  return <Tooltip headline="AI inference" tip="The execution phase where a trained model makes predictions on new data. When you prompt a model and it responds, that's inference.">inference</Tooltip>;
};

export const TrainingTooltip = () => {
  return <Tooltip headline="AI training" tip="The initial phase of AI model development, in which a model analyzes a dataset to learn patterns and relationships.">training</Tooltip>;
};

export const PyTorchTooltip = () => {
  return <Tooltip headline="PyTorch" tip="An open-source machine learning framework for building and training neural networks, widely used for deep learning research and production deployments." cta="Read the PyTorch documentation" href="https://pytorch.org/projects/pytorch/">PyTorch</Tooltip>;
};

export const DataCenterTooltip = () => {
  return <Tooltip headline="Data center" tip="A physical facility where Runpod's GPU, CPU, and storage hardware is located.">data center</Tooltip>;
};

<div className="overview-page-wrapper" />

Instant Clusters provide fully managed multi-node compute with high-performance networking for distributed workloads. Deploy <TrainingTooltip /> jobs or large-scale <InferenceTooltip /> without managing infrastructure, networking, or cluster configuration.

* **Scale beyond single machines**: Train models too large for one GPU, or accelerate training across multiple nodes.
* **High-speed networking**: 1600-3200 Gbps between nodes for efficient gradient synchronization and data movement.
* **Zero configuration**: Pre-configured static IPs, environment variables, and framework support.
* **On-demand**: Deploy in minutes, pay only for what you use.

## Get started

<CardGroup cols={3}>
  <Card title="Deploy a Slurm cluster" href="/instant-clusters/slurm-clusters" icon="splotch" horizontal>
    Managed Slurm for HPC workloads.
  </Card>

  <Card title="PyTorch distributed training" href="/instant-clusters/pytorch" icon="fire" horizontal>
    Multi-node PyTorch for deep learning.
  </Card>

  <Card title="Axolotl fine-tuning" href="/instant-clusters/axolotl" icon="screwdriver" horizontal>
    Fine-tune LLMs across multiple GPUs.
  </Card>
</CardGroup>

## How it works

Runpod provisions multiple GPU nodes in the same <DataCenterTooltip /> connected with high-speed networking. One node is designated primary (`NODE_RANK=0`), and all nodes receive pre-configured environment variables for distributed communication.

<div style={{ marginLeft: '4rem'}}>
  ```mermaid theme={"theme":{"light":"github-light","dark":"github-dark"}}
  %%{init: {'theme':'base', 'themeVariables': { 'primaryColor':'#9289FE','primaryTextColor':'#fff','primaryBorderColor':'#9289FE','lineColor':'#5F4CFE','secondaryColor':'#AE6DFF','tertiaryColor':'#FCB1FF','edgeLabelBackground':'#5F4CFE', 'fontSize':'15px','fontFamily':'font-inter'}}}%%

  flowchart TD
      internet["Internet"]
      eth0["eth0<br/>External traffic"]

      internet <--> eth0
      eth0 <--> primary

      subgraph cluster["Instant Cluster"]
          primary["Primary node<br/>NODE_RANK=0"]
          node1["Worker node<br/>NODE_RANK=1"]
          node2["Worker node<br/>NODE_RANK=2"]
          node3["Worker node<br/>NODE_RANK=3"]
          ens["ens1-ens8<br/>Up to 3200 Gbps"]

          primary <--> ens
          node1 <--> ens
          node2 <--> ens
          node3 <--> ens
      end

      style primary fill:#5F4CFE,stroke:#5F4CFE,color:#FFFFFF,stroke-width:2px
      style node1 fill:#4D38F5,stroke:#4D38F5,color:#FFFFFF,stroke-width:2px
      style node2 fill:#4D38F5,stroke:#4D38F5,color:#FFFFFF,stroke-width:2px
      style node3 fill:#4D38F5,stroke:#4D38F5,color:#FFFFFF,stroke-width:2px
      style internet fill:#22C55E,stroke:#22C55E,color:#000000,stroke-width:2px
      style eth0 fill:#fb923c,stroke:#fb923c,color:#000000,stroke-width:2px
      style ens fill:#9289FE,stroke:#9289FE,color:#FFFFFF,stroke-width:2px
      style cluster fill:#1B0656,stroke:#5F4CFE,color:#FFFFFF,stroke-dasharray: 5 5

      linkStyle default stroke-width:2px,stroke:#5F4CFE
  ```
</div>

The high-speed interfaces (`ens1`-`ens8`) handle inter-node communication for <PyTorchTooltip />, <TensorFlowTooltip />, and <SlurmTooltip />. The `eth0` interface on the primary node handles external traffic. See the [configuration reference](/instant-clusters/configuration) for environment variables and network details.

## Supported hardware

| GPU  | Network speed | Nodes                  |
| ---- | ------------- | ---------------------- |
| B200 | 3200 Gbps     | 2-8 nodes (16-64 GPUs) |
| H200 | 3200 Gbps     | 2-8 nodes (16-64 GPUs) |
| H100 | 3200 Gbps     | 2-8 nodes (16-64 GPUs) |
| A100 | 1600 Gbps     | 2-8 nodes (16-64 GPUs) |

For clusters larger than 8 nodes (up to 512 GPUs), [contact our sales team](https://ecykq.share.hsforms.com/2MZdZATC3Rb62Dgci7knjbA).

## Pricing

Pricing is based on GPU type and number of nodes. See [Instant Clusters pricing](https://www.runpod.io/pricing) for current rates.

Custom pricing is available for enterprise workloads. [Contact our sales team](https://ecykq.share.hsforms.com/2MZdZATC3Rb62Dgci7knjbA) for details.

<Note>
  All accounts have a default spending limit. To deploy larger clusters, contact [help@runpod.io](mailto:help@runpod.io).
</Note>