Get the best efficiency for your model training

We’ve built our cloud from the ground up with training resilience in mind. You’re getting the perfect infrastructure for large-scale model training — and a team of experts ready to help you with all your MLOps needs.

Set up your production-ready infrastructure in hours

The Nebius AI platform gives you the best experience with an intuitive cloud console and tools for ML/AI workloads like Kubernetes® and Terraform.

Fastest network for distributed training

Get the most out of multihost training on thousands of H100 GPUs of full mesh connection with latest InfiniBand network up to 3.2Tbit/s per host.

Best guaranteed uptime

Our platform provides a built-in self-healing system that allows VMs and hosts to restart within minutes instead of hours.

Scale up and down your capacity

With the on-demand payment model, you can dynamically scale your compute capacity via a simple console request. With long-term reserve, you get discounts on resources.

Everything you need for the best training performance

We provide an integrated stack for running distributed training that can be started with just two clicks.

Performance metrics for ML Training

Bus bandwidth in NCCL AllReduce

Max speed of filestore per node

Max speed of filestore per cluster

Intuitive cloud console for a smooth user experience

Manage your infrastructure and grant granular access to resources.

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Architects and expert support

Generative AI and distributed learning are emerging technologies, and you need a reliable partner on this journey. We test our platform with LLM pretraining to ensure everything runs smoothly.

Free of charge, we guarantee dedicated solution architect help and ensure 24/7 support for urgent cases.

Solution library and documentation

Nebius Architect Solution Library is a set of Terraform and Helm solutions designed to streamline the deployment and management of AI and ML applications on Nebius AI. It offers the tools and resources for your easy and efficient journey.

To make the most of the platform features, explore our comprehensive documentation for Nebius AI services.

Third party solutions for ML training

MLflow

MLflow is a platform for managing workflows and artifacts across the machine learning lifecycle.

Kubeflow

Kubeflow is an open-source platform dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable, and scalable.

Ray Cluster

Ray is an open-source distributed computing framework built for the deployment and orchestration of scalable distributed computing environments for a variety of large-scale AI workloads.

Tested by our in-house LLM team

The LLM team helps to enhance the efficiency of Nebius AI through dogfooding the cloud platform and delivering immediate feedback to the product and development team.

It supports the company’s ambition to be the most advanced cloud for AI explorers.

NVIDIA Collective Communication Library (NCCL) is designed to optimize inter-GPU communication. The AllReduce is a collective communication operation used to aggregate of model gradients across multiple GPU after every processed batch. 488 GB/s is a result for running tests in two nodes setup.

✻✻ The maximum limits of 64GB/s for reading and 32GB/s for writing are achievable for 1MiB random-access requests or 128KiB sequential-access requests if the storage is shared among 64 or more virtual machines and IO_redirect option is used to work with filestore.