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 provider-level failover routing when a node refuses or fails a job.

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

Platform safeguards

Usage metering interval

Every 15 min

Automatic instance shutdown at zero balance

Job pre-authorization

100% of estimated cost

402 before any node is contacted

Rate limiting

30 req/min per key

Deterministic 429 responses

Concurrency

3 jobs per key

429 until a slot frees

Support response

Same business day

Direct email escalation to the founding team

Payment security

PCI-DSS via Stripe

Card data never touches our servers