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Inference infrastructure, deployable now

Serving models is a stock problem, not an allocation problem: H200 NVL nodes ship from our shelves in days and serve 70–180B-class models with headroom.

Request a quote Inference stack options →
NVIDIA DGX Spark — inference development

Inference is where private infrastructure pays for itself fastest — steady loads, predictable scaling, and cloud GPU bills that outrun hardware cost in months. The H200 NVL's 141 GB per card is the practical sweet spot: big models, standard 2U servers, no exotic cooling.

Inference stack options

TierHardwareServes
DevelopmentNVIDIA DGX Spark — 128 GB unifiedPrototyping up to ~200B (quantized)
Single node2× H200 NVL in R760xa class70B-class production serving
Scaled node4× H200 NVL, NVLink-bridged180B-class / high concurrency
Inference pod4+ nodes, 400G fabric, load balancingMulti-model fleets, failover
Edge inferenceL40S / Jetson platformsLow-latency, on-site
Vector / RAG tierNVMe-heavy CPU nodesEmbeddings, retrieval, caches

Pricing and payback anchors

from $4kDGX Spark development tier
from $78k2× H200 NVL production node
Weeksfrom PO to serving — stocked path
6–12 motypical payback vs cloud at steady load

Stock rotates daily — positions are "typically available" and confirmed per request, usually within one business day. Stock guides →

Export compliance. NVIDIA H200/H100/B-series GPUs are US export-controlled dual-use items (ECCN 3A090). Haink supplies them only after end-user and destination screening under US EAR and OFAC rules, and declines any order to a restricted destination or end use. Hong Kong and Mainland China destinations are treated as controlled under current US rules; orders are quoted accordingly.

Frequently asked questions

What hardware do we need to serve a 70B model?

A 2× H200 NVL node (282 GB combined) serves 70B-class models comfortably at FP8/INT8 with production concurrency — from ~$78k, typically deployable within weeks from stock.

When does buying inference hardware beat cloud GPUs?

At sustained utilization: a $78k node versus typical cloud H100/H200 hourly rates pays back in roughly 6–12 months, then serves for years. We include this math, with your numbers, in every proposal.

Can we start small and scale?

Yes — the standard path: DGX Spark for development, one node for launch, NVLink pairs and then pods as load grows. Same software stack at every tier.

Do you supply the serving software too?

We supply hardware sized from measured throughput, and our AI team can deliver vLLM/TensorRT-LLM deployment, monitoring and MLOps on the same contract.

Private AI infrastructure → GPU hardware in stock → GPU servers Hong Kong → GPU servers Dubai →

Need serving capacity this quarter?

Pricing, availability and delivered lead time within one business day.

sales@haink.org