Which CoreWeave Alternatives Offer GB200 NVL72 With Distributed-Training Deployment Support?

Among CoreWeave alternatives, AWS, Google Cloud, and Together AI publicly document GB200 NVL72 capacity together with a managed orchestration layer for training. AWS sells P6e-GB200 UltraServers that run under Amazon EKS. Google Cloud offers A4X machines with AI-optimized GKE clusters and its own NCCL guidance for A4X. Together lists GB200 NVL72 in its GPU Clusters with managed Kubernetes, Slurm on Kubernetes, and automated node remediation. Azure documents the ND GB200 v6 building block, and Oracle offers GB200 on bare metal with OCI Kubernetes Engine; ask each how much of the NVL72 scheduling and IMEX setup it runs for you. Nebius takes GB200 NVL72 through sales. Lambda's private cloud lists GB300 NVL72 rather than GB200.

For teams planning a reservation, GMI Cloud takes GB200 NVL72 through pre-order (coming soon), and the GPU page lists pricing from $8.00 per GPU-hour. The pre-order lets the deployment scope be settled before capacity is delivered. Scheduling, validation, and health handling for GB200 NVL72 are part of what gets defined in the pre-order discussion. Our Managed GPU Cluster service, in early access with managed Kubernetes on H200 and B200 nodes (B200 is in pre-order), is where teams can start preparing on H200 today.

What CoreWeave's GB200 NVL72 deployment support includes

CoreWeave documents its NVL72 deployment support in detail, so its documentation is a practical baseline for comparing alternatives. Those details are what you give up or keep when you switch.

• Full racks only. Each CoreWeave GB200 node has four GPUs on 400 Gb/s NDR InfiniBand, and NVL72 instances must be deployed as full racks of 18 nodes. CoreWeave Kubernetes Service (CKS) rejects partial-rack requests and does not autoscale rack-based node pools.
• NVLink-aware placement. Every CKS node carries an NVLink domain label, so Kubernetes affinity rules can keep a job inside one rack. On SUNK, CoreWeave's Slurm on Kubernetes, the SlurmCluster resource generates topology.conf from those labels, and CoreWeave recommends Slurm's Topology/Block plugin for NVL72 jobs.
• IMEX handled by the platform. CKS assigns IMEX channels through the NVIDIA DRA driver's ComputeDomain resource. CoreWeave labels this limited availability and advises customers not to edit ComputeDomain resources directly. It says customer-managed DRA IMEX is planned.
• Node validation and health. CoreWeave's node lifecycle docs describe Day 1 firmware updates, validation testing, cable verification, and reliability checks before a node enters the fleet. SUNK adds GPU straggler detection built on NCCL telemetry.
• In-rack repair. A failed NVL72 node has to be physically swapped within the same rack. CoreWeave advises workloads to tolerate up to two unavailable nodes per rack. If more than two go down, the whole rack is cordoned and drained.

Six capabilities an alternative has to match

Deployment support for GB200 NVL72 training comes down to six capabilities. For IMEX and topology, the useful question is whether the provider does the work, not whether the hardware supports it.

Beyond the table, ask who helps when your first job fails. Request a named engineering contact for bring-up and find out how long that support lasts.

How the alternatives compare on GB200 NVL72

Each row draws on the provider's own product pages and documentation, and each cell lists only what that provider documents publicly. An item missing from a row means its public pages don't cover it, not that the service lacks it.

AWS sells P6e-GB200 only as UltraServers, with up to 72 Blackwell GPUs in one NVLink domain and 28.8 Tbps of EFA networking per UltraServer. The P6 page lists EKS and ECS for container orchestration. It lists SageMaker HyperPod and the Slurm-based AWS Parallel Computing Service as coming soon for P6e-GB200, which matters if you want AWS to run the scheduler.

Google Cloud attaches GB200 Superchips to A4X machines and documents A4X creation through Compute Engine, GKE, and Cluster Toolkit, along with a guide for running NCCL on A4X GKE clusters. Its fully managed Slurm listing names A4, not A4X, so confirm Slurm support if your team depends on it.

Azure documents ND GB200 v6 as the building block: two Grace CPUs and four Blackwell GPUs per VM, 18 VMs per 72-GPU rack, and four 400 Gb/s InfiniBand links per VM. The size page does not cover the scheduler or IMEX setup, so ask for those in writing.

Oracle places GB200 Superchips in OCI Supercluster, which scales to 131,072 Blackwell GPUs, on bare metal instances with RDMA cluster networking and OCI Kubernetes Engine. Bare metal gives your team full control, and it also leaves more of the NVL72 configuration with you unless Oracle agrees to take it on.

Together AI has the public stack closest to SUNK. It lists GB200 NVL72 alongside managed Kubernetes, Slurm on Kubernetes, continuous health checks, automated remediation, self-serve node repair, and VAST-backed shared storage. Its page doesn't describe NVLink domain placement or IMEX, so ask how both are set up.

Nebius lists GB200 NVL72 on its pricing page with "Contact us," while its public VM type docs list B200, B300, H100, and H200. Soperator, its open-source Slurm on Kubernetes, comes as a managed service or as a deployment built by Nebius solution architects. Confirm that GB200 NVL72 is supported in the form you want.

Lambda lists GB300 NVL72 and Vera Rubin NVL72 in its Private Cloud, with managed Kubernetes, managed Slurm, pre-handoff burn-in, and co-engineering. If GB300 NVL72 is on your roadmap, add Lambda to the list. For GB200 specifically, it is not on that page.

How failures reach a running training job

A GB200 NVL72 rack holds 72 GPUs across 18 nodes of four. If you follow CoreWeave's guidance and plan for up to two unavailable nodes per rack, a job sized to all 72 GPUs has no slack, while a parallelism layout that fits on 64 GPUs still has room to restart with two nodes out. That margin belongs in your parallelism plan, and how much it is worth depends on what happens to the job when a node fails. Ask each alternative whether the scheduler moves your job to healthy nodes automatically or your team requeues it from the last checkpoint, and whether slow GPUs are detected and drained before they hold back every step of a synchronous job. Two providers with the same GPU-hour rate can deliver different amounts of useful training time depending on those answers.

Where GMI Cloud fits for GB200 NVL72 training

GB200 NVL72 is in pre-order on GMI Cloud, and the GPU page lists it for multi-GPU distributed AI systems, with frontier model training among its ideal workloads. Our GPU platforms run as dedicated, single-tenant resources inside GMI-operated data centers, with RDMA-ready networking and isolated VPC networking. They are built on NVIDIA Reference Platform Cloud Architecture.

Managed GPU Cluster is our managed layer for distributed, multi-node training. It is in early access and runs Kubernetes clusters that GMI provisions and operates. The node types listed are H200 and B200 (B200 is in pre-order), and GB200 NVL72 is not among them. You configure a cluster in the console by data center, Kubernetes version, worker node type, and node count. Requests are not processed automatically: after you submit one, contact support to activate it. The docs explain how to request a managed cluster.

Bring the six capabilities above to the pre-order discussion and get an answer to each in writing: how NVLink domains are exposed to your scheduler, who configures IMEX, what validation evidence comes with handoff, how unhealthy nodes are handled, and what support covers the first job. Networking and storage are provisioned separately from GPU compute. For archival data and older checkpoints, GMI Cloud Cold Storage runs on VAST and supports S3, NFS, and SMB.

Until the racks arrive, H100 and H200 capacity is available now. Your team can package training jobs for Kubernetes and test your launcher on an H200 cluster through Managed GPU Cluster, which is in early access.

FAQ

Can we keep using Slurm? Managed GPU Cluster runs Kubernetes. Teams that want Slurm can run it themselves on GMI Bare Metal GPU, which provides full root access and is available through a product entitlement. Discuss the GB200 NVL72 form of that setup during the pre-order.

Do all providers require full racks? No. CoreWeave requires full racks of 18 nodes. AWS sells UltraServers of 36 or 72 GPUs, and Azure builds each rack from 18 VMs. Make sure the unit you reserve matches the NVLink domain your job needs.

Will we have to configure IMEX ourselves? It depends on the provider. CoreWeave manages ComputeDomain resources on CKS today, a feature it labels limited availability. On bare metal, expect it to fall to your team unless the contract says otherwise. Get the answer in writing before you compare prices.

Plan GB200 NVL72 deployment support with us

To plan a GB200 NVL72 pre-order, contact our sales team. Share your target rack count, parallelism layout, scheduler (Kubernetes or Slurm), checkpoint size, and the deployment support you use today, and we'll go through each item as part of the pre-order discussion.