Instant Clusters

Instant Clusters enables high-performance computing across multiple machines with high-speed networking capabilities.

Key characteristics:

• Fast local networking with bandwidths from 100 Gbps to 3200 Gbps within a single data center
• Static IP assignment for each pod in the cluster
• Environment variables set automatically for coordination between nodes

Deploy your first Instant Cluster

This guide explains how to use Instant Clusters to support larger workloads.

Each pod receives a static IP on the overlay network. The system designates one machine as the primary node by setting PRIMARY_IP and CLUSTER_IP environment variables. This primary designation simplifies working with multiprocessing libraries that require a primary node.

Environment variables

The following environment variables are available in all pods:

Environment Variable	Description
PRIMARY_ADDR / MASTER_ADDR	The address of the primary pod
PRIMARY_PORT / MASTER_PORT	The port of the primary pod (all ports are available)
NODE_ADDR	The static IP of this pod within the cluster network
NODE_RANK	The cluster rank assigned to this pod (set to 0 for primary)
NUM_NODES	Number of pods in the cluster
NUM_TRAINERS	Number of GPUs per pod
HOST_NODE_ADDR	Defined as PRIMARY_ADDR:PRIMARY_PORT for convenience

The variables MASTER_ADDR/PRIMARY_ADDR and MASTER_PORT/PRIMARY_PORT are equivalent. The MASTER_* variables provide compatibility with tools that expect these legacy names.

Network interfaces

High-bandwidth interfaces (eth1, eth2, etc.) handle inter-node communication, 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 eth1 to enable launching and bootstrapping distributed processes.

Example PyTorch implementation

export NCCL_SOCKET_IFNAME=eth1
torchrun \
  --nproc_per_node=$NUM_TRAINERS \
  --nnodes=$NUM_NODES \
  --node_rank=$NODE_RANK \
  --master_addr=$MASTER_ADDR \
  --master_port=$MASTER_PORT \
  main.py

note

All accounts have a default spending limit. To launch a larger cluster, submit a support ticket at help@runpod.io

Applications

Instant Clusters benefit these use cases:

Deep Learning & AI

• Training Large Neural Networks: Speed up deep learning by distributing data across GPUs for faster convergence
• Federated Learning: Train models across distributed systems while maintaining data privacy

High-Performance Computing (HPC)

• Scientific Simulations: Run weather forecasting, molecular dynamics, and climate modeling with multi-GPU acceleration
• Astrophysics & Space Exploration: Simulate galaxy formations, detect gravitational waves, and model space weather
• Fluid Dynamics & Engineering: Perform computational fluid dynamics in aerospace, automotive, and energy sectors

Gaming & Graphics Rendering

• Ray Tracing & Real-Time Rendering: Create ultra-realistic graphics for gaming, VR, and movie CGI
• Game Development & Testing: Render game environments, test AI-driven behaviors, and generate procedural content
• Virtual Reality & Augmented Reality: Deliver real-time multi-view rendering for immersive experiences

Large-Scale Data Analytics

• Big Data Processing: Accelerate data processing in AI-driven analytics and recommendation systems
• Social Media Analysis: Detect real-time trends, analyze sentiment, and identify misinformation

note

You can review your spending in the Clusters tab in the billing section.