Runpod × OpenAI: Parameter Golf challenge is live
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Run multi-node GPU clusters in minutes with high-performance networking and no setup overhead, with flexible pricing for on-demand and reserved capacity.
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Up to 200+ GPUs
on-demand
10,000+ GPUs
reserved
InfiniBand + RoCE
v2 networking
Slurm-ready + PyTorch & Axolotl
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CLUSTER OPTIONS
Start instantly or reserve dedicated capacity for long-term workloads.
High-performance networking for distributed AI training.
High-bandwidth, low-latency networking for distributed training.
RDMA over Ethernet for flexible, high-performance workloads.
High-speed communication across nodes at scale.
Compatible with NCCL, MPI, DeepSpeed, Axolotl, and more.
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Tools and infrastructure designed for how teams actually run clusters.
Run distributed workloads with built-in scheduling and resource management.
Track GPU, memory, and disk usage from a single dashboard.
Add or scale nodes without rebuilding your cluster.
Persistent storage accessible across nodes for large datasets and models.
Direct access for debugging, setup, and workflow control.
Bring your own Docker images and manage your full software stack.
Runpod Clusters support distributed training, inference, research, and compute-heavy workloads that require more scale, coordination, and performance than a single machine can provide.
Train large models across multi-node GPU clusters at scale.
Fine-tune models on large datasets using on-demand or reserved clusters.
Serve models across multiple nodes for high-throughput inference.
Run experiments, evaluations, and RL workloads on distributed compute.
Run rendering, simulation, and multi-node compute workloads at scale.
Process large datasets and embeddings beyond single-node limits.
Tools and infrastructure designed for how teams actually run clusters.
Certified for security, availability, and confidentiality.
HIPAA-compliant environments available for regulated workloads.
Supports GDPR requirements for organizations operating in the EU.
Isolated environments for strict data governance and separation.
Runpod Clusters support both on-demand and reserved capacity, giving teams a clear path from fast experimentation to committed infrastructure at scale.
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Work directly with our team to design infrastructure, pricing, and support tailored to your production requirements.
Single-tenant cluster infrastructure fully reserved for your workloads.
Secure a baseline GPU allocation with options to burst as demand increases.
Access committed pricing and volume discounts for sustained workloads.
Uptime guarantees and contractual SLAs designed for production environments.
Direct access to engineering support, escalation paths, and onboarding assistance.
SOC 2, BAA, and DPA documentation, along with flexible contract structures.
Tell us about your workload and GPU requirements. Our team typically follows up within one business day.
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Deploy a cluster