FLAGSHIP • ACCELERATED INFRAIB 3.2TbpsK8s • Slurm • Bare Metal

GPU-accelerated infrastructure, without the hyperscaler hangover.

Burst a single 4090 for tonight's experiment — or reserve a 64×H100 InfiniBand fabric for your foundation run. Pre-tuned PyTorch, vLLM, and Kubernetes. SSH in minutes, scale to production when ready.

Reserve capacity →See pricing

Live pricing • hourly / monthly / reserved

Transparent $/GPU/hr. No quota lottery.

Accelerator	VRAM & Interconnect	Ideal accelerated workloads	Hourly	Monthly	Reserved 12-mo
H100 SXM 8×	640GB HBM3 • NVLink • IB	Frontier training, 70B+ inference	$2.49	$1,699	$1,399
H200 NVL 8×	1.1TB HBM3e • IB	Long-context, MoE, 405B	$3.49	$2,399	$1,999
A100 80GB 8×	640GB • NVLink	Fine-tuning workhorse	$1.99	$1,299	$1,049
L40S	48GB Ada • PCIe	vLLM serving, rendering	$1.19	$799	$649
RTX 4090	24GB • PCIe	LoRA, prototypes	$0.69	$449	$369

Storage: 500GB NVMe included, $0.08/GB-mo after. Egress: first 5TB free. Startup + academic credits available.

Instant estimate — size your accelerated run

GPU type GPUs Hours

Estimated: $—

List estimate. Reserved + spot bring 30–60% off. Get firm quote →

Solution 01 • Inference factory

Serve 10× cheaper on your own accelerated tier.

vLLM + TensorRT-LLM tuning — continuous batching, quantization (AWQ/FP8), speculative decoding
Autoscaling + caching — semantic cache hit 35-55%, p95 <1.2s for 8B, <3.5s for 70B
OpenAI-compatible endpoint — swap base_url, keep SDKs
Distillation pipeline — GPT-4 → private 8B that runs on 1×L40S

$ math your CFO will love

Path	10M tok/day cost
Frontier API	~$15,000/mo
Hyperscaler GPU VM	~$4,800/mo
Zhang Alpha Research L40S + distilled 8B	~$1,600/mo

Solution 02 • Training fabric

Multi-node training that scales linearly.

NCCL-tested IB fabrics, synchronous checkpointing to NVMe → S3, PyTorch DDP / FSDP / DeepSpeed templates. We profile MFU on day one and publish it.

SlurmRayKubeFlowWeKa-style NVMeInfiniBand NDRWeights & BiasesCheckpoint resume

Solution 03 • Kubernetes for AI

GPU-aware Kubernetes, done right.

➤ Scheduling

Time-slicing, MIG partitioning, gang scheduling, quota + preemption for teams.

➤ GitOps

ArgoCD, Terraform, image pipelines, canary rollouts for model versions.

➤ Observability

DCGM metrics, token/latency dashboards, cost per team, paging.

How to start

From DM to DGX-class in 4 steps.

Tell us the workload

Model, tokens/day, latency, compliance needs.

Get blueprint + quote

Diagram + benchmarks + $/1M tokens in 24h.

Spin up

SSH in minutes, cluster in 1–2 days.

Scale or hand over

We operate or train your team.

Reserve H100/H200 capacity

Q3–Q4 fills fast. $500 fully-credited deposit holds 8× nodes for 14 days.

Hold my nodes →

Or mention “GPU HOLD” in the contact form.