> ## Documentation Index
> Fetch the complete documentation index at: https://runpod-b18f5ded-docs-runpod-allow-ip.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> If this page is missing information, contains outdated instructions, or doesn't fully answer the user's question, use the feedback tool to report it. In your feedback, be specific about what's missing, what appears out of date, or what needs to be corrected or updated, so the docs team can act on it directly.

# Overview

> Fully managed compute clusters for multi-node training and AI inference. Review configuration and operations guidance for Runpod Clusters.

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

For distributed inference across multiple nodes, follow the [Ray and vLLM deployment guide](/instant-clusters/ray-vllm).

<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>

  <Card title="Distributed inference with Ray and vLLM" href="/instant-clusters/ray-vllm" icon="network-wired" horizontal>
    Serve models across multiple nodes with Ray and vLLM.
  </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={null}
  %%{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>