Enterprise compute

A control plane built for multi-node training, not a Docker wrapper.

kwctl handles node discovery, dynamic MIG partitioning, and pre-flight GPU health gating so a failing card never takes down a 512-GPU run.

kwctl

$ kwctl cluster create --nodes 16 --gpu b200-nvlink --network infiniband-3.2t

Bare-Metal Kubernetes, Ray & Slurm

Pre-configured Ray clusters, Slurm workload scheduling, and bare-metal Kubernetes operators tuned for multi-node distributed training.

Non-blocking leaf-spine fabric at 3.2 Tbps with GPUDirect RDMA so GPUs read and write remote VRAM without traversing CPU or host RAM.

Proprietary lightweight orchestration for automated node discovery, dynamic MIG partitioning, and pre-flight health checks that isolate failing GPUs.

Long-run resilience with distributed checkpoint offload and automated node replacement or migration inside a 15-minute SLA window.

Cluster architectures

Architecture	Scale	Fabric	Power	Workload
GB200 NVL72 Rack	72 GPUs · 1 rack	NVLink 5 domain + NDR IB	~120 kW liquid-cooled	Frontier-scale foundation model training
B200 SXM Pod	16 nodes · 128 GPUs	3.2 Tbps non-blocking InfiniBand	100 kW/rack direct-to-chip	Multi-node pre-training with RDMA
H200 Inference Fleet	8 nodes · 64 GPUs	RoCE v2 lossless Ethernet	60 kW/rack hybrid loop	High-volume LLM serving at low TTFT
H100 Training Cell	32 nodes · 256 GPUs	Quantum-2 InfiniBand leaf-spine	45 kW/rack	Distributed pre-training & fine-tuning

Service levels

Network fabric availability

99.99%

Automatic service credits

Node availability

99.99%

Automatic service credits

Hardware fault recovery

< 15 min

Auto replacement or migration

Sev-1 support response

< 15 min

Dedicated TAM escalation

Tenant isolation

Single-tenant

Dedicated switches + VPC peering

Encryption

At rest & in transit

Hardware-level key isolation