Private AI Infrastructure Supplier — On-Premise GPU Clusters, AI Servers, and AI Networking

Haink supplies private AI infrastructure to enterprises in Hong Kong, Dubai, UAE, and Mainland China. Private AI infrastructure means GPU compute, high-speed interconnect, all-flash storage, and AI networking deployed on-premise or in a colocation facility — under the enterprise's direct control, without dependency on public cloud GPU availability or cloud provider pricing. Haink sources AI hardware from NVIDIA, Supermicro, HPE, Dell, and Lenovo, and delivers configured AI infrastructure to enterprise clients across Asia-Pacific and the Middle East.

What Is Private AI Infrastructure?

Private AI infrastructure is the physical hardware stack required to train, fine-tune, and run large AI models on dedicated compute — owned or leased by the enterprise, not shared with other organizations as in public cloud. A private AI infrastructure deployment consists of four layers:

GPU Compute Layer

The GPU servers are the core of private AI infrastructure. For large language model training and fine-tuning, enterprises deploy multi-GPU servers using NVIDIA H100 SXM5 (NVLink 4.0, 900 GB/s GPU-to-GPU bandwidth), NVIDIA H200 SXM5 (141 GB HBM3e memory), NVIDIA B200 SXM5 (NVLink 5.0, 1.8 TB/s, 192 GB HBM3e), or NVIDIA B300 SXM (Blackwell Ultra, 288 GB HBM3e — 50% more memory than B200, designed for frontier model pre-training at 1T+ parameter scale). For AI inference deployment — running trained models for production queries — NVIDIA H100 PCIe, L40S, and A100 PCIe configurations offer cost-effective throughput. Standard configurations: 4-GPU, 8-GPU, and 16-GPU nodes; clusters scale from a single 8-GPU node for small-scale fine-tuning to 64-node (512 GPU) clusters for pre-training workloads.

AI Networking and Interconnect Layer

Multi-node GPU clusters require high-bandwidth, low-latency interconnect between servers. NVIDIA InfiniBand HDR (200 Gb/s) and NDR (400 Gb/s) are the standard for large-scale AI training clusters, providing the bandwidth necessary for distributed training across nodes. For clusters where InfiniBand cost is prohibitive, 100GbE or 400GbE RDMA over Converged Ethernet (RoCEv2) provides a cost-effective alternative — adequate for inference clusters and small training clusters. Front-end Ethernet (management, storage access, out-of-band) is typically provided by Cisco or Aruba switching.

Storage Layer

AI workloads are storage-intensive: loading training datasets into GPU memory, checkpointing model states during long training runs, and serving model weights for inference all require high-throughput storage. Private AI infrastructure deployments use all-flash NVMe storage: NetApp AFF, Pure Storage FlashArray, or HPE Alletra for enterprise storage with AI-optimized performance profiles. Direct-attached NVMe (U.2 or E1.S drives inside GPU servers) handles local dataset staging; shared parallel file storage handles the dataset repository accessed by all nodes in a cluster.

Power and Cooling Layer

High-density GPU infrastructure has power and cooling requirements that exceed standard data center specifications. A single 8x H100 SXM5 server consumes 10.2 kW at full load. A 16-node cluster draws over 160 kW. NVIDIA B200 SXM5 servers require direct liquid cooling (DLC) — air cooling is insufficient for B200 thermal output. Private AI infrastructure deployments require pre-qualification of the data center facility: power density per rack, cooling type (air vs. DLC vs. rear-door heat exchanger), UPS capacity, and generator backup. Haink evaluates facility readiness as part of pre-deployment assessment for AI cluster orders.

Private AI vs. Cloud AI: When Private Makes Sense

The decision between private AI infrastructure and cloud GPU (AWS, Azure, GCP, Alibaba Cloud) is primarily economic, with security and availability as secondary factors.

Cost Crossover Point

Cloud GPU instances (NVIDIA H100 on AWS) run approximately USD 20–35 per GPU-hour at on-demand pricing, or USD 12–18 per GPU-hour at 1-year reserved pricing. An 8-GPU private AI server (Supermicro SYS-821GE with 8x H100 SXM5) costs approximately USD 350,000–420,000 fully configured. At 60–70% average GPU utilization over a 3-year depreciation period, the cost per GPU-hour of private infrastructure is typically USD 2–5 — five to ten times lower than reserved cloud pricing. For enterprises running AI workloads more than 6–8 hours per GPU per day, private AI infrastructure pays back within 12–18 months versus cloud GPU costs.

When Cloud AI Is Preferred

Cloud GPU makes sense for: irregular workloads with low average utilization (below 30%), short-duration experiments where hardware commitment is premature, organizations without data center capacity to host high-density GPU infrastructure, and use cases requiring rapid scaling beyond owned capacity. Many enterprises run a hybrid model: private AI infrastructure for steady-state production workloads and fine-tuning, cloud GPU for burst training and experimentation.

Data Sovereignty and Security

For financial services, government, healthcare, and other regulated enterprises in Hong Kong, UAE, and Mainland China, data residency requirements or security policy may mandate that AI training data and model weights never leave the organization's controlled infrastructure. Private AI infrastructure satisfies data sovereignty requirements by design — data does not traverse public cloud networks and is not resident on shared infrastructure. This is a decisive factor for many enterprise AI deployments in regulated industries across the regions Haink serves.

Private AI Infrastructure Deployment Sizes

Single-Node AI Server — Entry Point

An 8-GPU server (NVIDIA H100 SXM5 or PCIe) is the entry point for serious enterprise AI — adequate for fine-tuning models up to 70 billion parameters, running multiple concurrent inference workloads, or small-scale training on proprietary datasets. Single-node deployments require minimal data center changes and install in a standard rack. Lead time: 4–8 weeks for standard configurations. Typical cost range: USD 300,000–500,000 for a fully configured 8x H100 SXM5 node with networking and storage.

Small AI Cluster — 4 to 8 Nodes (32–64 GPUs)

Appropriate for fine-tuning 70B–405B parameter models, running parallel training experiments, or hosting multiple AI services with dedicated GPU allocation. This scale requires InfiniBand or 400GbE interconnect between nodes and a dedicated storage backend. Haink provides end-to-end delivery: hardware sourcing, rack layout design, cabling, and initial system configuration. Typical cost range: USD 1.5M–4M depending on GPU model and storage configuration.

Large AI Cluster — 32+ Nodes (256+ GPUs)

Deployed by enterprises undertaking AI model pre-training, large-scale inference serving, or building internal AI platforms as a shared enterprise resource. These deployments require full data center planning (power, cooling, network topology), dedicated InfiniBand fabric with spine-leaf architecture, parallel distributed storage, and Kubernetes-based cluster management. Haink works with enterprise clients and their data center operations teams to plan and source the complete infrastructure stack.

Who Deploys Private AI Infrastructure

Across Hong Kong, Dubai, UAE, and Mainland China, private AI infrastructure is deployed by financial services firms running proprietary AI models on confidential trading or risk data; enterprise AI teams building internal LLM capabilities on proprietary knowledge bases; research institutions and universities requiring sustained GPU compute at a cost cloud pricing makes prohibitive; government agencies with data sovereignty requirements that preclude cloud AI; and technology companies that have grown past the cloud GPU cost crossover point.

Haink Private AI Infrastructure Supply

Haink sources and delivers private AI infrastructure from the vendors with the strongest AI hardware portfolios:

NVIDIA: DGX H100, DGX H200, DGX B200, HGX platform for OEM AI servers, InfiniBand NDR networking, NVLink switches, GB300 NVL72 rack-scale
Supermicro: SYS-821GE series (8x H100/H200 SXM5), AS-8125GS series (8x H100/H200 PCIe), ARS-821GL-NHR (8x B200/B300 SXM) — Supermicro density-optimized designs are a primary choice for GPU cluster builds
HPE: ProLiant Compute Scale-Up Server 3000, Cray EX platforms for the largest deployments, HPE Alletra storage
Dell: PowerEdge XE9680 (8x H100/H200 SXM5), PowerEdge R760xa, PowerScale storage for AI datasets
Lenovo: ThinkSystem SR680a V3 (8x H100 SXM5), Neptune liquid cooling — particularly relevant for high-density deployments in Mainland China

Haink delivers to data center facilities in Hong Kong, Singapore, UAE (Dubai and Abu Dhabi), and Mainland China (Beijing, Shanghai, Shenzhen). For enterprise clients requiring configuration and rack-and-stack services, Haink coordinates with local installation partners in each market.

Related Resources

Frequently Asked Questions

What does private AI infrastructure cost?

Entry-level private AI infrastructure — a single 8x NVIDIA H100 SXM5 server with InfiniBand networking and all-flash NVMe storage — starts at approximately USD 350,000–500,000. A small cluster (32 GPUs, 4 nodes) runs USD 1.5M–2.5M. A mid-scale cluster (128 GPUs, 16 nodes) with full InfiniBand fabric and parallel storage runs USD 6M–12M, depending on GPU model (H100 vs H200 vs B200) and storage configuration. B200-based infrastructure carries a 40–60% premium over equivalent H100 configurations but delivers approximately 2.5–4x the AI training throughput. B300 (Blackwell Ultra, 288 GB HBM3e) carries a further premium over B200 and targets frontier-scale deployments. Contact Haink for current pricing on specific configurations.

How long does it take to deploy private AI infrastructure?

Single-node deployments: 4–10 weeks from purchase order to operational system. Small clusters (4–8 nodes): 8–16 weeks, including facility pre-qualification, hardware delivery, rack-and-stack, InfiniBand cabling, and system software configuration. Large clusters (32+ nodes): 16–32 weeks minimum. Lead times for NVIDIA H200 and B200 SXM5 systems remain extended in 2025–2026 due to sustained global demand — early engagement with Haink on procurement timeline is recommended.

What is the minimum GPU configuration for running a 70B LLM?

A 70-billion parameter model in FP16 precision requires approximately 140 GB of GPU memory. This fits across two NVIDIA H100 SXM5 (80 GB HBM2e each) for inference, or runs on a single H200 (141 GB HBM3e). For fine-tuning (which requires optimizer states and activations in addition to weights), 4–8 H100 SXM5 GPUs are required. A single 8x H100 SXM5 node is the practical entry point for fine-tuning 70B models on proprietary data.

Can I run a 405B or 671B parameter model on private infrastructure?

A 405-billion parameter model (Llama 3.1 405B) in FP16 requires approximately 810 GB of GPU memory — roughly 10 H100 SXM5 GPUs or 6 H200 GPUs. In practice this means a minimum of two 8-GPU nodes with InfiniBand for inference. The 671B DeepSeek R1 model has similar requirements. B200 systems (192 GB HBM3e per GPU) allow running 405B–671B models on fewer nodes. Private inference of frontier-scale models is feasible with 2–4 node deployments.

Does Haink supply AI infrastructure to Mainland China?

Haink supplies AI infrastructure in Mainland China within the constraints of current export control regulations. NVIDIA H100, H200, and B200 SXM5 GPUs are subject to BIS export restrictions for China. NVIDIA has released China-compliant variants (H20, L20, L2) specifically designed to comply with export control performance thresholds. Haink advises on compliant GPU options for Mainland China deployments and sources the appropriate hardware for the regulatory environment. For enterprises in Mainland China requiring maximum AI performance within regulatory compliance, Lenovo and Huawei domestic AI server platforms are also available through Haink.

What is the difference between AI training and AI inference infrastructure?

Training infrastructure is optimized for maximum GPU-to-GPU bandwidth — distributed training requires constant gradient exchanges across GPUs, which is why InfiniBand and NVLink SXM are standard. Inference infrastructure is optimized for GPU memory capacity and throughput per dollar — running trained models for production queries requires holding large model weights in memory and serving many simultaneous requests efficiently. H100 SXM5 nodes are typically used for training; H100 PCIe, L40S, and A100 PCIe nodes are common for cost-optimized inference deployments.