Buy AI Hardware: NVIDIA GPUs, GPU Servers and Enterprise Storage for AI

We supply three categories of professional AI hardware: AI GPUs (NVIDIA H100, H200, A100, RTX PRO 6000 Blackwell, RTX 5090, T1000, L4 and more), AI Servers (DGX H100/H200, HGX OEM nodes, GB200 NVL72, and custom multi-GPU workstation and rack-mount platforms), and Enterprise Storage (Seagate EXOS 20–24 TB nearline HDDs, WD Gold/Ultrastar, Samsung PM9A3 NVMe SSDs, Seagate Nytro, and Micron 7450 series). All items ship with free worldwide DDP delivery.

The H100 and H200 share the same Hopper GPU die and identical compute performance (~3.9 PFLOPS FP8 sparse). The H200 replaces HBM2e with HBM3e, gaining 141 GB of capacity (vs 80 GB) and 4.8 TB/s bandwidth (vs 3.35 TB/s) — critical for inference serving of large models. Both H100 and H200 are available in SXM (NVSwitch, high-bandwidth multi-GPU) and PCIe (standard server) form factors. The RTX PRO 6000 Blackwell is a workstation/rack GPU using Blackwell architecture: 96 GB of ECC GDDR7 in a dual-slot PCIe card, 5th-generation Tensor Cores with FP4 support, but lower aggregate FLOPS than H100/H200. It is the best choice when memory capacity in a PCIe slot is the priority.

NVSwitch is a dedicated ASIC that forms a full-mesh interconnect between all GPUs in an AI server system. In a DGX H100, 4 NVSwitch ASICs connect 8 H100 GPUs so that every GPU can communicate with every other GPU at 900 GB/s simultaneously — far exceeding what PCIe can deliver. Without NVSwitch, multi-GPU training requires AllReduce operations over PCIe (~64 GB/s), which becomes a severe bottleneck for tensor and model parallel workloads on large models. NVSwitch is available only in SXM-format servers (DGX, HGX).

As a baseline: for fine-tuning a 7–13B model on a custom dataset, 1–4 TB of NVMe SSD is sufficient for the dataset, checkpoints, and model weights. For pre-training or fine-tuning 70B+ models, plan for 10–100 TB of fast storage depending on your dataset scale. At petabyte scale (full pre-training runs), a distributed storage cluster using Seagate EXOS or WD Ultrastar nearline HDDs in a Ceph/Lustre configuration is standard. We recommend a tiered architecture: enterprise NVMe for hot I/O (checkpointing, active batches) and high-capacity HDDs for the dataset lake.

It depends on precision. At FP16 a 70B model requires ~140 GB of VRAM — only the H200 (141 GB) can hold it on one GPU at full precision. At INT8 quantisation (~70 GB), the H200 or an H100 80 GB handles it with room for KV cache. At INT4 (GGUF Q4, ~35–40 GB), the RTX PRO 6000 Blackwell (96 GB) runs it comfortably with plenty of KV cache headroom. At INT4 with extreme quantisation (~20–25 GB), even an RTX 5090 (32 GB) can serve 70B models.

Yes. We offer free worldwide DDP shipping on all orders. DDP (Delivered Duty Paid) means we handle export, international freight, customs clearance and import duty payment — the price you see is the price you pay. GPU and storage orders ship within 1–3 business days. Large server systems may require additional lead time for configuration and freight; contact us for a specific timeline.

Yes. MillionMiner handles enterprise, research institution, and cloud infrastructure procurement. Volume pricing is available for multi-unit GPU orders, pallet-quantity storage drives, and complete server systems. Contact our sales team with your requirements — model, quantity, delivery location, and timeline — and we will provide a dedicated quote with freight logistics included.

Entry-level 2-GPU PCIe servers draw 2–4 kW and can use standard air cooling. An 8-GPU DGX H100 draws up to 10.2 kW and requires dual 30A, 240V circuits (North America) or 3-phase 32A (Europe). The GB200 NVL72 draws up to 120 kW per rack and requires direct liquid cooling and a dedicated 3-phase power feed — facility planning is mandatory before ordering. For individual GPU cards, the RTX PRO 6000 Blackwell draws 300 W and fits in any standard workstation with a sufficient PSU.

Our team is available 24/7 via WhatsApp (+49 176 777 888 33), email and phone. We assist with GPU selection for specific workloads, multi-GPU cluster design, server platform comparisons, storage architecture planning, and B2B procurement. Contact us or visit our FAQ for instant answers.