Runpod offers custom Instant Cluster pricing plans for large scale and enterprise workloads. If you’re interested in learning more, contact our sales team.
Runpod Instant Clusters provide fully managed compute clusters with high-performance networking for distributed workloads like multi-node training and large-scale AI inference.

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Key features

  • High-speed networking from 1600 to 3200 Gbps within a single data center.
  • On-demand clusters are available from 2-8 nodes (16-64 GPUs)
  • Contact our sales team for larger clusters (up to 512 GPUs).
  • Supports H200, B200, H100, and A100 GPUs.
  • Automatic cluster configuration with static IP and environment variables.
  • Multiple deployment options for different frameworks and use cases.

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Networking performance

Instant Clusters feature high-speed local networking for efficient data movement between nodes:
  • Most clusters include 3200 Gbps networking.
  • A100 clusters offer up to 1600 Gbps networking.
This fast networking enables efficient scaling of distributed training and inference workloads. Runpod ensures nodes selected for clusters are within the same data center for optimal performance.

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Zero configuration

Runpod automates cluster setup so you can focus on your workloads:
  • Clusters are pre-configured with static IP address management.
  • All necessary environment variables for distributed training are pre-configured.
  • Supports popular frameworks like PyTorch, TensorFlow, and Slurm.

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Get started

Choose the tutorial that matches your preferred framework and use case. Deploy a Slurm cluster: Set up a managed Slurm cluster for high-performance computing workloads. Slurm provides job scheduling, resource allocation, and queue management for research environments and batch processing workflows. Deploy a PyTorch distributed training cluster: Set up multi-node PyTorch training for deep learning models. This tutorial covers distributed data parallel training, gradient synchronization, and performance optimization techniques. Deploy an Axolotl fine-tuning cluster: Use Axolotl’s framework for fine-tuning large language models across multiple GPUs. This approach simplifies customizing pre-trained models like Llama or Mistral with built-in training optimizations. Deploy an unmanaged Slurm cluster: For advanced users who need full control over Slurm configuration. This option provides a basic Slurm installation that you can customize for specialized workloads.
All accounts have a default spending limit. To deploy a larger cluster, submit a support ticket at help@runpod.io.

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Network interfaces

High-bandwidth interfaces (ens1, ens2, etc.) handle communication between nodes, while the management interface (eth0) manages external traffic. The NCCL environment variable NCCL_SOCKET_IFNAME uses all available interfaces by default. The PRIMARY_ADDR corresponds to ens1 to enable launching and bootstrapping distributed processes. Instant Clusters support up to 8 interfaces per node. Each interface (ens1 - ens8) provides a private network connection for inter-node communication, made available to distributed backends such as NCCL or GLOO.

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Environment variables

The following environment variables are present in all nodes in an Instant Cluster:
Environment VariableDescription
PRIMARY_ADDR / MASTER_ADDRThe address of the primary node.
PRIMARY_PORT / MASTER_PORTThe port of the primary node. All ports are available.
NODE_ADDRThe static IP of this node within the cluster network.
NODE_RANKThe cluster rank (i.e. global rank) assigned to this node. NODE_RANK = 0 for the primary node.
NUM_NODESThe number of nodes in the cluster.
NUM_TRAINERSThe number of GPUs per node.
HOST_NODE_ADDRA convenience variable, defined as PRIMARY_ADDR:PRIMARY_PORT.
WORLD_SIZEThe total number of GPUs in the cluster (NUM_NODES * NUM_TRAINERS).
Each node receives a static IP address (NODE_ADDR) on the overlay network. When a cluster is deployed, the system designates one node as the primary node by setting the PRIMARY_ADDR and PRIMARY_PORT environment variables. This simplifies working with multiprocessing libraries that require a primary node. The following variables are equivalent:
  • MASTER_ADDR and PRIMARY_ADDR
  • MASTER_PORT and PRIMARY_PORT.
MASTER_* variables are available to provide compatibility with tools that expect these legacy names.

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NCCL configuration for multi-node training

For distributed training frameworks like PyTorch, you must explicitly configure NCCL to use the internal network interface to ensure proper inter-node communication: 
export NCCL_SOCKET_IFNAME=ens1
Without this configuration, nodes may attempt to communicate using external IP addresses in the 172.xxx range, which are reserved for internet connectivity only. This will result in connection timeouts and failed distributed training jobs in your cluster.
When troubleshooting multi-node communication issues, also consider adding debug information: 
export NCCL_DEBUG=INFO

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When to use Instant Clusters

Instant Clusters offer distributed computing power beyond the capabilities of single-machine setups. Consider using Instant Clusters for:
  • Multi-GPU language model training: Accelerate training of models like Llama or GPT across multiple GPUs.
  • Large-scale computer vision projects: Process massive imagery datasets for autonomous vehicles or medical analysis.
  • Scientific simulations: Run climate, molecular dynamics, or physics simulations that require massive parallel processing.
  • Real-time AI inference: Deploy production AI models that demand multiple GPUs for fast output.
  • Batch processing pipelines: Create systems for large-scale data processing, including video rendering and genomics.