NVIDIA Supplier — H100, H200, B200, B300, L40S, RTX Ada, DGX, InfiniBand

Haink supplies NVIDIA AI and data center hardware to enterprises, research institutions, cloud providers, and AI teams in Hong Kong, Dubai, and Mainland China. The NVIDIA portfolio available through Haink includes data center GPU platforms (H100, H200, B200, B300), NVIDIA L40S inference GPUs, NVIDIA RTX Ada Generation professional GPUs for workstations, NVIDIA DGX personal and enterprise AI systems, and NVIDIA InfiniBand and Ethernet networking for high-performance AI clusters.

NVIDIA is the dominant supplier of AI training and inference compute globally, with H100 and H200 installed in the majority of the world's largest AI data centers and H100 SXM5 being the most widely deployed GPU for large language model training. NVIDIA's CUDA ecosystem — the software platform that runs PyTorch, TensorFlow, and all major AI frameworks — has a 10+ year head start over competing GPU platforms, making NVIDIA GPUs the default choice for AI infrastructure in most enterprise and research deployments.

Data Center GPUs — Hopper Generation

NVIDIA H100

• NVIDIA H100 SXM5 80GB — the most widely deployed AI training GPU; 3,958 TFLOPS FP8, 3.35 TB/s memory bandwidth, NVLink 4.0 (900 GB/s bidirectional), 700W TDP; installed in DGX H100, Supermicro SYS-821GE, Dell XE9680, HPE Cray XD670 8-GPU SXM server platforms
• NVIDIA H100 PCIe 80GB — 3,958 TFLOPS FP8, 2 TB/s memory bandwidth, PCIe Gen5, 350W TDP; installed in standard PCIe server platforms (Supermicro SYS-420GP, SYS-220HE, SYS-111E) for inference clusters and training workloads where NVLink bandwidth is not the bottleneck
• NVIDIA H100 NVL — dual H100 on a single PCIe card with NVLink bridge providing 188 GB combined VRAM for single-card inference of very large models

NVIDIA H200

• NVIDIA H200 SXM5 141GB — same GH100 Hopper die as H100 with 141 GB HBM3e (vs H100's 80 GB HBM2e); 4.8 TB/s memory bandwidth; identical FP8 compute to H100; primary use case is inference of 70B+ models that require more than 80 GB GPU memory, and memory-bandwidth-bound training workloads
• NVIDIA H200 PCIe NVL 141GB — PCIe form factor H200 for inference servers; enables single-GPU serving of 70B models that previously required two H100 PCIe cards

Data Center GPUs — Blackwell Generation

NVIDIA B200

• NVIDIA B200 SXM5 192GB — 9,000 TFLOPS FP8 / 18,000 TFLOPS FP4, 8 TB/s HBM3e memory bandwidth, NVLink 5.0 (1,800 GB/s bidirectional), ~1,000W TDP; Blackwell architecture delivers 2.3× more FP8 compute than H100 SXM5; requires direct liquid cooling at full utilization; the current-generation primary GPU for new large-scale AI training cluster deployments
• New in Blackwell: FP4 precision (first in NVIDIA data center GPU line), NVLink 5.0 with 2× H100 bandwidth, RAS Engine for proactive error detection, Confidential Computing hardware memory encryption

NVIDIA B300 (Blackwell Ultra)

• NVIDIA B300 SXM — 288 GB HBM3e (50% more than B200), higher FP8 throughput than B200, NVLink 5.0; DLC mandatory; designed for frontier model pre-training at 1T+ parameter scale and highest-concurrency inference of large deployed models
• Available in the same 8-GPU SXM server form factor as B200 (Supermicro ARS-821GL-NHR and equivalent platforms)

NVIDIA GB200 NVL72

• NVIDIA GB200 NVL72 — rack-scale architecture combining 36 Grace CPU modules and 72 B200 GPU dies in a single NVLink 5.0 domain; 130 TB/s total NVLink fabric bandwidth; the entire rack operates as a single unified compute domain for model parallelism; requires full rack direct liquid cooling; designed for training frontier models and serving the largest deployed models at hyperscale; available through NVIDIA DGX GB200 NVL72 rack systems

NVIDIA L40S — Inference and Visualization

• NVIDIA L40S 48GB — Ada Lovelace architecture, 48 GB GDDR6 ECC, 362 TFLOPS FP8, PCIe Gen4 passive cooling (server form factor); designed for AI inference, video transcoding, and professional visualization in rackmount servers; the bridge between professional workstation GPUs (RTX Ada) and data center training GPUs (H100)
• L40S does not require liquid cooling, fits in standard PCIe server slots, and provides substantially more inference throughput than RTX 6000 Ada at lower cost than H100; optimal for AI inference servers serving 20–200 concurrent users on 7B–34B models
• Deployed in Supermicro SYS-111E (2× L40S 1U), SYS-221GE (4× L40S 2U), and standard PCIe servers from Dell and HPE

NVIDIA RTX Ada Generation — Professional Workstation GPUs

• NVIDIA RTX 4000 Ada Generation — 20 GB GDDR6 ECC, entry professional GPU for AI workstations, 3D design, and CAD workflows; supports local inference of 7B–13B models
• NVIDIA RTX 4500 Ada Generation — 24 GB GDDR6 ECC, mid-range professional GPU; improved performance over RTX 4000 Ada for AI workstation and design workloads
• NVIDIA RTX 5000 Ada Generation — 32 GB GDDR6 ECC, PCIe 4.0 x16; runs 34B models at quantization for small team AI inference workstations
• NVIDIA RTX 6000 Ada Generation — 48 GB GDDR6 ECC, the highest-VRAM RTX Ada workstation GPU; runs 70B models at 4-bit quantization on a single card; two RTX 6000 Ada in NVLink provides 96 GB for higher-quality 70B inference; Haink's primary recommendation for AI workstation deployments requiring maximum local LLM capability
• NVIDIA RTX 5000 Ada and RTX 6000 Ada both support NVLink bridging for dual-GPU configurations in tower workstations

NVIDIA DGX Systems

NVIDIA DGX Spark

• NVIDIA DGX Spark — personal AI supercomputer powered by NVIDIA GB10 Grace Blackwell Superchip; 128 GB unified LPDDR5X memory shared between Grace ARM CPU and Blackwell GPU; 1 PFLOPS FP8 AI compute; compact desktop form factor; standard 100–240V power; ships with NVIDIA AI Enterprise stack (NIM, CUDA, TensorRT-LLM) pre-installed; runs 70B models at full FP16 precision; recommended for individuals and small teams running local LLMs, RAG, and AI development without data center infrastructure

NVIDIA DGX H100

• NVIDIA DGX H100 — 8× NVIDIA H100 SXM5 80 GB GPUs, dual Intel Xeon Platinum 8480C CPUs, 2 TB DDR5 system RAM, 8× ConnectX-7 400G InfiniBand, 30 TB NVMe storage; factory-integrated NVIDIA-validated AI training appliance; the reference platform for H100-based AI training infrastructure

NVIDIA DGX H200

• NVIDIA DGX H200 — 8× NVIDIA H200 SXM5 141 GB GPUs in the same DGX chassis as DGX H100; drop-in upgrade providing 76% more GPU memory for inference of larger models and memory-bandwidth-bound training workloads

NVIDIA DGX B200

• NVIDIA DGX B200 — 8× NVIDIA B200 SXM5 192 GB GPUs, Grace CPU modules, NVLink 5.0 interconnect, ConnectX-8 InfiniBand networking; next-generation DGX platform delivering 2.3× more FP8 compute than DGX H100; requires direct liquid cooling infrastructure

NVIDIA DGX GB200 NVL72

• NVIDIA DGX GB200 NVL72 — complete liquid-cooled rack system containing 36 GB200 Superchip modules (72 B200 GPU dies + 36 Grace CPU cores); 130 TB/s NVLink 5.0 fabric; factory-integrated, pre-cabled, and pre-configured by NVIDIA; the flagship product for AI training cluster deployments at hyperscale

NVIDIA InfiniBand Networking

NVIDIA InfiniBand is the dominant interconnect for AI training clusters, providing GPU-to-GPU communication bandwidth for distributed training across nodes. InfiniBand's RDMA (Remote Direct Memory Access) capability allows GPUs in different servers to communicate directly without CPU involvement, reducing communication overhead during all-reduce operations in distributed LLM training.

• NVIDIA ConnectX-7 400G InfiniBand — 400 Gbps HDR200/NDR200 InfiniBand HCA; installed in DGX H100 and DGX H200 systems; the standard InfiniBand NIC for H100/H200 cluster nodes
• NVIDIA ConnectX-8 800G InfiniBand — 800 Gbps NDR InfiniBand HCA; installed in DGX B200 and next-generation GPU clusters
• NVIDIA QM9700 / QM9790 InfiniBand switches — 64-port NDR 400G InfiniBand switches for building fat-tree or dragonfly InfiniBand fabrics for AI training clusters
• NVIDIA QM8790 HDR 200G InfiniBand switches — for H100 cluster InfiniBand fabrics
• NVIDIA Spectrum-X — Ethernet-based alternative to InfiniBand for AI training clusters using RoCEv2 (RDMA over Converged Ethernet); provides InfiniBand-class performance over standard 400G and 800G Ethernet infrastructure

NVIDIA Software Stack

NVIDIA hardware value is inseparable from the CUDA software ecosystem — the primary reason NVIDIA maintains its AI infrastructure dominance:

• CUDA — parallel computing platform and API running all major AI frameworks (PyTorch, TensorFlow, JAX); 10+ years of optimization and third-party library development
• cuDNN — deep neural network library providing GPU-accelerated primitives for convolution, normalization, and activation operations
• TensorRT — inference optimization and deployment SDK; converts trained models to optimized engines for production inference
• TensorRT-LLM — inference optimization library specifically for large language models; enables multi-GPU tensor parallelism and FP8/FP4 quantized inference for LLMs
• NVIDIA NIM (NVIDIA Inference Microservices) — pre-packaged, optimized inference containers for major LLMs (Llama, Mistral, Nemotron) ready for production deployment
• NCCL (NVIDIA Collective Communications Library) — GPU-to-GPU communication library for distributed training all-reduce operations over InfiniBand and Ethernet

Where Haink Supplies NVIDIA Hardware

• Hong Kong — NVIDIA GPU servers, DGX systems, L40S, RTX Ada workstation GPUs, and InfiniBand networking delivered duty-free through Hong Kong's free port. NVIDIA GPU supplier Hong Kong →
• Dubai — NVIDIA AI infrastructure delivered through Dubai free trade zone logistics for UAE enterprise and data center deployments, with onward distribution to MENA. NVIDIA GPU supplier Dubai →
• Mainland China — NVIDIA GPU hardware availability for Mainland China is subject to current US export control regulations; certain high-performance GPUs (H100, H200, B200) are restricted for export to China. Haink advises on compliant GPU server configurations available for Mainland China delivery. GPU server supplier Mainland China →

Related Resources

• Supermicro — Primary NVIDIA GPU Server Platform
• Dell — PowerEdge XE9680 AI Server
• HPE — Cray XD670 AI Server
• H100 vs H200 vs B200 vs B300 Comparison
• AI Server Supplier
• GPU Infrastructure
• AI Workstation for Small Teams
• Enterprise AI Infrastructure
• All IT Hardware Brands

Frequently Asked Questions

Who supplies NVIDIA H100 servers in Hong Kong?

Haink supplies NVIDIA H100 SXM5 and H100 PCIe GPU servers — on Supermicro SYS-821GE-TNHR (8× H100 SXM5), Supermicro SYS-420GP-TNR, and other validated platforms — to enterprises and data centers in Hong Kong. Hong Kong's free port status means no import duties on NVIDIA GPU hardware. Haink coordinates sourcing and direct delivery to enterprise facilities and colocation data centers in Hong Kong.

Who supplies NVIDIA GPU servers in Dubai?

Haink supplies NVIDIA H100, H200, B200, L40S, and RTX Ada GPU servers to enterprises, cloud providers, and AI organizations in Dubai and the UAE. Haink handles procurement coordination and delivery through Dubai free trade zone logistics with onward distribution across the Middle East and Africa.

What is the difference between NVIDIA H100 and B200?

H100 (Hopper) delivers 3,958 TFLOPS FP8 with 80 GB HBM2e and NVLink 4.0. B200 (Blackwell) delivers 9,000 TFLOPS FP8 / 18,000 TFLOPS FP4 with 192 GB HBM3e and NVLink 5.0 — 2.3× more FP8 compute, 2.4× more memory, 2× more NVLink bandwidth. B200 also introduces FP4 precision and requires direct liquid cooling at full utilization. See the full H100 vs H200 vs B200 comparison.

What is NVIDIA L40S and when should I use it instead of H100?

NVIDIA L40S is a 48 GB GDDR6 Ada Lovelace GPU for AI inference and professional visualization in standard PCIe rackmount servers. L40S does not require liquid cooling and costs substantially less than H100 per GPU. It is the right choice for AI inference serving (deploying trained models to users) rather than AI training. A 1U server with two L40S GPUs (96 GB total VRAM) can serve 70B models at 4-bit quantization to small-to-medium teams at lower infrastructure cost than H100 SXM servers. For large-scale training, H100 or B200 SXM is required.

What is NVIDIA DGX Spark and who is it for?

NVIDIA DGX Spark is a personal AI supercomputer powered by the GB10 Grace Blackwell Superchip with 128 GB unified memory, capable of running 70B parameter models at full FP16 precision in a compact desktop form factor. It is designed for AI researchers, developers, and small teams who need serious local AI compute without data center infrastructure. DGX Spark plugs into a standard power outlet and ships with the complete NVIDIA AI software stack pre-installed.

Can NVIDIA GPUs be exported to Mainland China?

US export control regulations restrict export of certain high-performance NVIDIA GPUs to Mainland China, including H100, H200, A100, and similar data center GPUs above specific performance thresholds. NVIDIA has developed China-specific variants (H20, L20, L2) with reduced performance to comply with export regulations. Haink advises on currently compliant GPU server configurations available for Mainland China delivery on a per-inquiry basis, as regulations and available configurations change.

What InfiniBand switches does Haink supply for AI clusters?

Haink supplies NVIDIA QM9700 and QM9790 NDR 400G InfiniBand switches for H100 and B200 AI training cluster fabrics, and NVIDIA QM8790 HDR 200G switches for existing H100 HDR cluster deployments. InfiniBand switch procurement is coordinated alongside GPU server platform procurement for complete AI training cluster builds.