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NVIDIA — H100, H200, B200, B300 GPU servers for private AI

The complete NVIDIA AI infrastructure portfolio: SXM training clusters, inference servers, DGX systems, InfiniBand networking, and RTX Ada workstations — supplied with OEM warranty to Hong Kong, Dubai, and Mainland China.

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H100 SXM5 H200 SXM5 B200 SXM5 B300 SXM DGX H100 / H200 / B200 L40S InfiniBand NDR

GPU generations at a glance

Four generations — different memory, compute, and cooling requirements. Pick the one that fits the workload, budget, and facility.

H100 SXM5
H200 SXM5
B200 SXM5
B300 SXM
Architecture
Hopper
Hopper
Blackwell
Blackwell Ultra
GPU Memory
80 GB HBM2e
141 GB HBM3e
192 GB HBM3e
288 GB HBM3e
FP8 TFLOPS
3,958
3,958
9,000
9,000+
NVLink BW
900 GB/s
900 GB/s
1,800 GB/s
1,800 GB/s
Cooling
Air or DLC
Air or DLC
DLC required
DLC required
Best for
Training 7B–70B, proven ecosystem
70B+ inference, memory-bound training
New training clusters, maximum throughput
1T+ pre-training, frontier inference
Lead times:H100 SXM5: 4–10 weeks  ·  H200 SXM5: 6–12 weeks  ·  B200 SXM5: 12–20 weeks  ·  B300 SXM: 14–24 weeks. Contact Haink for current allocation status. Check availability →

What we supply

Training H100 / H200 SXM5 GPU servers Supermicro SYS-821GE-TNHR, Dell XE9680, HPE Cray XD670 — 8-GPU SXM platforms for LLM training and fine-tuning. NVLink 4.0 fabric, ConnectX-7 InfiniBand.
Training B200 / B300 SXM GPU servers Supermicro ARS-821GL-NHR — 8-GPU Blackwell platforms. 2.3× H100 FP8 throughput. DLC-mandatory. For new AI training cluster builds and frontier inference.
Inference L40S inference servers Supermicro SYS-221GE (4×), SYS-111E (2×) with NVIDIA L40S 48 GB Ada. No liquid cooling required. Best cost-per-token for 7B–70B model serving.
DGX NVIDIA DGX H100 / H200 / B200 Factory-integrated 8-GPU AI appliances with 2 TB DDR5, 30 TB NVMe, 8× ConnectX-7/8 InfiniBand NICs. NVIDIA-validated, zero integration risk.
DGX DGX GB200 NVL72 Rack-scale 72-GPU Blackwell platform with 130 TB/s NVLink 5.0 fabric. Factory-integrated full-rack DLC system. For frontier model pre-training and hyperscale inference.
Workstation DGX Spark + RTX Ada DGX Spark (GB10, 128 GB unified) for personal AI. RTX 4000/5000/6000 Ada Generation for AI workstations — from 20 GB to 48 GB GDDR6 ECC.
Networking InfiniBand NDR switches NVIDIA QM9700 / QM9790 (64-port NDR 400G) for AI training cluster fabrics. ConnectX-7 and ConnectX-8 HCAs. SHARP in-network computing for allreduce offload.
Networking Spectrum-X Ethernet AI fabric 400G and 800G RoCEv2 Ethernet alternative to InfiniBand. NVIDIA Spectrum-4 switches with adaptive routing for AI training clusters where InfiniBand cost is a constraint.

Why NVIDIA leads enterprise AI infrastructure

The hardware advantages matter — but the real moat is CUDA, which has a 10+ year head start on every competing GPU platform.

CUDA ecosystem PyTorch, TensorFlow, JAX, TensorRT, NCCL — every major AI framework is built and optimized for NVIDIA CUDA first. Competitor GPU platforms run CUDA workloads in compatibility mode, not natively.
NVLink bandwidth NVLink 5.0 at 1,800 GB/s GPU-to-GPU (B200/B300) is 28× the bandwidth of PCIe Gen5. Distributed training all-reduce latency drops proportionally. Multi-node clusters require InfiniBand for between servers.
HBM memory HBM3e provides 8 TB/s memory bandwidth (B200) vs ~2 TB/s for GDDR6-based GPUs. Memory bandwidth, not raw FLOPS, is the throughput bottleneck for transformer inference — making HBM decisive for LLM performance.
Software stack TensorRT-LLM, NIM inference microservices, NCCL, DCGM, Base Command Manager — NVIDIA ships optimized production software alongside every GPU. Competing hardware ships GPUs; NVIDIA ships a platform.

Common questions

Who supplies NVIDIA H100 servers in Hong Kong?
Haink supplies NVIDIA H100 SXM5 and H100 PCIe GPU servers to enterprises in Hong Kong. Hong Kong's free port status means zero import duty on NVIDIA hardware. Standard 8× H100 SXM5 configurations on Supermicro SYS-821GE-TNHR or Dell XE9680 are available with 4–10 week lead times.
Who supplies NVIDIA GPU servers in Dubai and UAE?
Haink supplies NVIDIA H100, H200, B200, and B300 GPU servers to enterprises and data centers in Dubai and the UAE. Full-specification servers — no export control restrictions apply to UAE. Hardware is delivered through Dubai free trade zone logistics.
What is the difference between H100, H200, B200, and B300?
H100 SXM5 (80 GB HBM2e, 3,958 TFLOPS FP8) — the proven training workhorse with the broadest software support. H200 SXM5 (141 GB HBM3e, same compute) — best for 70B+ inference where memory is the constraint. B200 SXM5 (192 GB HBM3e, 9,000 TFLOPS FP8, NVLink 5.0) — 2.3× faster than H100, DLC-mandatory. B300 SXM Blackwell Ultra (288 GB HBM3e) — maximum memory and compute for 1T+ frontier model training.
Does B200 require liquid cooling?
Yes — NVIDIA B200 and B300 at full training utilization require direct liquid cooling (DLC). Air cooling is insufficient for sustained B200/B300 thermal output. H100 and H200 can run in air-cooled configurations, though DLC improves thermal headroom. Haink advises on DLC facility requirements for Blackwell-generation GPU cluster orders.
Can NVIDIA GPUs be delivered to Mainland China?
US BIS export regulations restrict export of H100, H200, B200, and B300 to Mainland China. NVIDIA's China-compliant variants — H20, L20, L2 — are available within regulatory thresholds. Haink advises on compliant GPU configurations for Mainland China delivery on a per-inquiry basis, as regulations evolve. Lenovo and Huawei domestic AI server platforms are also available through Haink for China deployments.
What is the minimum hardware to fine-tune a 70B LLM?
For LoRA fine-tuning of a 70B model: 4× H100 SXM5 (320 GB total). For full supervised fine-tuning (SFT) in BF16: 8× H100 SXM5 or 4× H200 SXM5 with gradient checkpointing. A single 8× H100 SXM5 node (DGX H100 or equivalent) is the practical entry point for most enterprise fine-tuning use cases up to 70B parameters.

Need NVIDIA GPU servers?

Send the configuration — GPU generation, node count, and target region. Firm pricing and availability within one business day.

sales@haink.org

Go deeper

Technical guides and deployment resources from the Haink knowledge base.

H100 vs H200 vs B200 vs B300 →Detailed GPU spec comparison — architecture, memory, TFLOPS, and when to choose each.
Private AI Infrastructure →Complete guide to on-premise GPU infrastructure — costs, deployment sizes, cloud vs private analysis.
NVLink vs PCIe GPU →When NVLink SXM matters for training and when PCIe is sufficient for inference.
Liquid Cooling for AI Servers →DLC, immersion, rear-door heat exchangers — requirements by GPU generation.
AI Infrastructure Cost Guide →GPU cluster costs, TCO, and ROI vs cloud GPU — with real pricing ranges.
GPU Server Buying Guide →Decision framework: workload → GPU → server platform → network → facility checklist.