Dedicated GPU Clusters
for serious AI training

A single GPU rental gives you one card on one host. A GPU cluster is multiple servers — each with 4 or 8 GPUs — connected by NVLink (within a node) and InfiniBand or RoCE (between nodes). Your training job sees them as a unified pool of compute and memory. Renting one GPU works for inference and small fine-tunes; clusters are for distributed training, 70B+ pre-training, RLHF, and HPC simulations that won't fit on a single card.

H100 if your workload benefits from FP8 (modern training, frontier inference) or needs NVLink 4.0 and 3.35 TB/s memory bandwidth. A100 if you're on a mature CUDA stack with predictable workloads (7B–34B fine-tuning, classical deep learning, production inference) — it's the cost-efficient default. A 32-GPU H100 cluster typically trains a model in roughly half the time of a 32-GPU A100 cluster, so factor in time-to-checkpoint, not just hourly rate.

Self-serve clusters (up to 64 GPUs in available regions) provision in 1–4 hours with pre-built CUDA, PyTorch, vLLM, and NCCL stacks. Reserved clusters of 128+ GPUs with custom networking or bare-metal isolation take 24–72 hours from contract signing. Compare that with hyperscaler quotes that often run 6–12 weeks for the same configuration.

Yes. Managed Kubernetes ships with the NVIDIA device plugin, MIG slicing, GPU autoscaler, DCGM metrics, and Prometheus add-ons. Control plane is HA across 3 zones. GPU node pools bill at the same per-hour rate as our GPU as a Service — there's no Kubernetes upcharge on the compute. Bring your existing Helm charts, KServe, or Kubeflow workloads.

Within a node: NVLink 4.0 at 900 GB/s on H100 SXM. Between nodes: 3.2 Tb/s of NDR InfiniBand or 400 GbE RoCE v2, non-blocking rail-optimised topology. SHARP in-network reductions handle collective ops without round-tripping through GPU memory. On 70B-class training workloads we typically see 92–96% scaling efficiency from 8 to 256 GPUs, depending on the model architecture.

Shared GPU rental is fine for development, experiments, and inference where you don't care if another tenant is on the same host. Dedicated clusters matter when (a) regulatory compliance requires single-tenancy, (b) you need predictable performance for long training runs without noisy-neighbour variance, or (c) you're running BYOK encryption or air-gapped workloads. Our dedicated clusters are bare-metal — the host is yours.

Yes. We pre-wire Slurm, Ray, and SkyPilot, but you're not locked in. Bare-metal clusters give you root access and a clean OS image — install whatever scheduler your team standardises on. Most teams pick Slurm for HPC-style workflows, Ray for elastic ML jobs, and K8s when they need general-purpose orchestration alongside the GPU jobs.

Dedicated clusters run on single-tenant bare-metal with encrypted-at-rest volumes and BYOK support. We're SOC 2 Type II and ISO 27001 certified, with DPDP-compliant India data residency available on request. No data leaves your assigned region without explicit configuration. For regulated industries (financial services, healthcare), we offer air-gapped deployments with a customer-managed VPN.