Products / Dedicated AI Clusters
A private cluster, designed around your models.
Single-tenant GPU clusters with compute, networking, storage, and scheduler engineered together, then validated against your workloads before your first production job.
- Tenancy
- Single-tenant: servers, network, and storage
- Networking
- NVLink scale-up; InfiniBand or RoCE scale-out
- Scheduler
- Kubernetes or Slurm
- Handoff
- Validated with your own jobs
Cluster design
In distributed training, the slowest link sets the pace.
A congested network, an overloaded storage tier, or a slow checkpoint path can leave expensive GPUs idle. We design each layer around how your models communicate, read data, and recover.
Compute and scale-up
GPU systems, host resources, and NVLink domain size matched to your model and parallelism strategy.
Scale-out networking
InfiniBand or Ethernet with RoCE, using RDMA between servers, designed around your communication patterns and failure domains.
Storage and data paths
Shared file systems, object storage, and local NVMe, with fast checkpoint writes and restores.
Bring-up and validation
Health checks, performance tests, and representative jobs, measured against criteria you approve in advance.
Orchestration
Use the scheduler your team already knows.
Either way, you set the projects, queues, quotas, and priorities that let several teams share one cluster.
Kubernetes
Containers for training services, model serving, and shared AI platforms.
- Good for
- Teams that ship containers and want training and serving on one control plane
- You set
- Namespaces, images, access policies, cluster services
- Operated by
- Your team, or ARC under Managed AI Infrastructure
Slurm
Batch scheduling for distributed training, HPC, and research workflows.
- Good for
- Research teams with established batch workflows and large multi-node jobs
- You set
- Partitions, queues, resource policies, job environments
- Operated by
- Your team, or ARC under Managed AI Infrastructure
Dedicated is not the same as managed
Your hardware. Your choice of operator.
Dedicated describes tenancy: the servers, network fabric, and storage serve only your organization, and you define the software stack. Who operates the cluster is a separate decision. Your team can run it, or ARC can through Managed AI Infrastructure.
Managed AI InfrastructureResponsibilities
Clear ownership at every layer.
Agreed before deployment, so nobody has to work it out during an incident.
| Layer | ARC handles | You decide |
|---|---|---|
| Physical systems | Integration, commissioning, validation, hardware lifecycle | Configuration, growth plan, acceptance criteria |
| Networking and storage | Topology, data paths, telemetry | Data access and checkpoint requirements |
| Orchestration | Kubernetes or Slurm, when ARC operates it | Projects, users, queues, images, policies |
| Applications | Outside ARC’s scope | Models, data, application logic, releases |
Design the cluster around the work, not the rack count.
Tell us about your model, scale, scheduler, and timeline. We’ll come back with a reference architecture and a delivery plan.
or email contact@arccloudai.com