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Runpod

Build the future, not infrastructure. The all-in-one cloud platform to train, fine-tune, and deploy AI effortlessly.

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Overview

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Runpod is a specialised, Docker-native GPU cloud platform that streamlines the AI/ML workflow for engineers and teams. By providing bare-metal access to high-performance GPUs, Runpod enables users to train, fine-tune, and deploy AI models rapidly and cost-effectively. The platform overcomes infrastructure challenges such as cold starts and scaling complexities with near-instant deployment and auto-scaling, serving as a flexible compute backbone from experiments to enterprise AI applications. Its globally distributed cloud resources support both single-GPU and multi-node training across diverse regions.

How It Works

  • GPU Cloud (Pods):
    • Rapidly deploy Docker container-based GPU instances.
    • Choose Secure Cloud for reliability or Community Cloud for cost savings.
    • Select On-Demand Pods for persistent workloads or Spot Pods for lower-cost, interruptible compute.
  • Serverless:
    • Use pay-per-second serverless compute with autoscaling.
    • Flex workers scale to zero when idle; Active workers offer uninterrupted, discounted compute.
  • Runpod Hub:
    • Browse, deploy, and share preconfigured AI repos directly from GitHub in one click, skipping manual setup.
  • Runpod CLI:
    • Manage GPU Pods and Serverless endpoints programmatically for a seamless, code-focused workflow.
  • Instant Clusters:
    • Spin up multi-node GPU environments with high-speed networking for distributed training and large model inference.

Use Cases

AI Inference at Any Scale
Serve inference for image, text, audio, and LLMs (like Llama 3 7B) from research prototypes to production APIs, with scalable, cost-effective GPU resources.
Cost-Effective Model Training & Fine-Tuning
Rapidly train and fine-tune custom AI models using powerful GPUs, flexible billing, and compute credits tailored for startups and researchers.
Real-Time & Batch AI Deployment
Run demanding, compute-heavy workloads – from vision model deployments to batch inference and intelligent agents – without infrastructure bottlenecks.

Features & Benefits

  • Autoscale in seconds; scale from zero to thousands of workers instantly
  • Zero cold-starts with always-on active workers
  • Sub-200ms cold-starts with FlashBoot
  • 30-second deployment for fast iteration
  • Fast by default with real-time caching systems
  • Persistent data storage and no data egress fees
  • Docker-native architecture for custom container deployment
  • Pay-per-second billing and zero idle costs
  • Unlimited data processing with no ingress/egress fees
  • Save up to 90% on infrastructure compared to traditional cloud
  • Compute credits for startups and researchers (up to $25K)
  • Global deployment across 8+ regions
  • Multi-node GPU clusters with robust networking (up to 8 nodes, 64 GPUs)
  • Slurm support for cluster workload management
  • Comprehensive monitoring, logs, and real-time metrics
  • Seamless GitHub deployments and instant rollback
  • Secure by default & pursuing SOC2, HIPAA, GDPR certifications
  • Runpod Hub and CLI for enhanced developer experience

Target Audience

  • Engineers and developers building full-stack AI applications
  • Machine Learning (ML) engineers seeking fast model training and deployment
  • Early-stage startups needing cost-effective compute and startup credits
  • ML researchers requiring on-demand, high-performance GPU for experimentation
  • Solo researchers working on single-GPU tasks
  • Enterprise-scale teams managing large, global AI/ML workloads with reliability and security
  • Hobbyists looking for affordable GPU compute for machine learning projects

Pricing

  • Pay-per-second GPUs:
    • Available from $0.00011 per second (Serverless), with GPU Cloud billed by the minute.
    • No ingress/egress fees.
  • GPU Cloud Pricing Examples (Per-hour):
    • H200 SXM (141GB): $3.59/hr
    • H100 PCIe (80GB): $1.99/hr
    • A100 PCIe (80GB): $1.19/hr
    • L40S (48GB): $0.79/hr
    • RTX A6000 (48GB): $0.33/hr
    • RTX 3090 (24GB): $0.22/hr
  • Serverless Pricing (Per-second):
    • H100 PRO: $0.00116 (Flex), $0.00093 (Active) for 80GB
    • A100: $0.00076 (Flex), $0.00060 (Active) for 80GB
    • L40, L40S, 6000 Ada PRO: $0.00053 (Flex), $0.00037 (Active) for 48GB
    • L4, A5000, 3090: $0.00019 (Flex), $0.00013 (Active) for 24GB
  • Storage:
    • Network Storage: $0.05/GB/month
    • Volume/Container Disk: $0.10/GB/month (running), $0.20/GB/month (idle)
    • Persistent Network Storage: $0.07/GB/month (<1TB), $0.05/GB/month (>1TB)
  • Compute Credits: Early-stage startups and ML researchers can apply for up to $25K in free compute credits.
  • Reservations: Additional savings available with long-term commitments on certain worker types.

FAQs

What is Runpod Hub?

Runpod Hub is a centralised catalogue of preconfigured AI repositories that you can browse, deploy, and share. All repositories are optimised for Serverless deployment, enabling you to get a running endpoint in minutes.

Why should I use Runpod Hub instead of deploying my own containers manually?

Runpod Hub offers one-click deployment with prebuilt Docker images and Serverless handlers, removing the need to write Dockerfiles or manage dependencies. It provides a UI for configuration and built-in tests, significantly reducing deployment time.

Who benefits from using Runpod Hub?

End users and developers can run popular AI models with no setup. Hub creators can showcase open-source work, and enterprises/teams can adopt standardised, production-ready AI endpoints to onboard fast.

How do I deploy a repository from the Hub?

In the Runpod console, go to the Hub, select or search for a repository, review details and requirements, and click "Deploy". The endpoint is live in minutes.

What is the difference between a GPU pod and an Instant Cluster?

A GPU pod is a single-node instance with one or more GPUs; an Instant Cluster connects multiple nodes via high-speed networking for distributed workloads requiring greater scale.

How is billing handled for Instant Clusters?

Instant Clusters are billed by the second—just like regular GPU pods. Billing ceases when the cluster is terminated, with no upfront or minimum commitments.

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