AI and data center infrastructure.
Enterprise NVIDIA GPUs, AI servers, storage and high-speed networking, from a single workstation to a full AI factory. Designed, delivered and supported by NanoTK.
Pick the tier that matches your workload today. Scale up without changing platform.
Edge & Desk Inference
AI workstations for teams that want to run models locally.
- NVIDIA DGX Spark (1 PFLOP, 128 GB unified memory)
- DGX Station, ASUS Ascent GX10, Dell Pro Max
- GB300 deskside systems
GPU Servers Inference + Fine-tuning
PCIe and HGX servers from 2 to 10 GPUs for departmental and enterprise AI.
- Supermicro, HPE ProLiant, Dell PowerEdge, Lenovo
- HGX H200 / B200 / B300 8-GPU systems
- NVIDIA DGX H100 / H200 / B200 / B300 BasePOD
Rack Scale Inference + Fine-tuning + Training
Liquid-cooled AI factories for training frontier-scale models.
- NVIDIA GB200 / GB300 NVL72 (72 GPUs per rack)
- Vera Rubin NVL72
- NVIDIA Groq 3 LPX
Every building block of an AI data center.
From a single GPU to a complete validated architecture.
NVIDIA GPUs
PCIe cards, SXM modules and HGX baseboards for AI training, inference and visualization.
- H200 141 GB, H100 80 / 94 GB
- A100 40 / 80 GB, L40S, L4
- RTX Pro 6000 Blackwell, RTX 5090
Server Memory
DDR5 ECC RDIMM for AI, mission-critical and in-memory database workloads.
- 32 GB to 256 GB modules
- 5600 and 6400 MT/s
- Samsung, Micron, SK hynix
Enterprise NVMe
PCIe Gen5 enterprise and data-center SSDs with GPU-direct storage support.
- 3.84 TB to 15.36 TB
- E3.S and 2.5" U.2 / U.3
- Samsung, Micron, KIOXIA
High-Speed Networking
400G and 800G optical interconnects plus AI-fabric switching.
- NVIDIA / Mellanox transceivers, OSFP and QSFP112
- Quantum-2 InfiniBand QM9700 / QM9790
- H3C RoCE Ethernet S9827 / S9855
AI Storage
Enterprise storage tuned for 400 Gb/s InfiniBand and GPU-direct NVMe.
- Huawei OceanStor Dorado 3000 to 18000
- DDN ES/AI400X2, SFA400X2, GRIDScaler
- Block, parallel file and object storage
Reference Architectures
Validated compute, storage and in-band / out-of-band network designs.
- RoCE vs InfiniBand fabric design
- DGX SuperPOD and BasePOD architectures
- Spine / leaf AI networking
Edge to cloud, end to end.
We do not just ship boxes. Every deployment follows the same six steps.
Assess
Workload sizing and requirements review
Design
Reference architecture, pricing and budget
Procure
Genuine hardware from authorized channels
Deploy
Installation, cabling and commissioning
Train
Hands-on training for your technical staff
Support
Ongoing maintenance and 24/7 support
For the engineers.
Expand a section to see the exact models we can source. Availability and part numbers are confirmed per project.
NVIDIA GPU line-up
| GPU | Memory | Form factor | Architecture | Best for |
|---|---|---|---|---|
| H200 NVL | 141 GB HBM3e | PCIe | Hopper | Large LLM inference |
| H100 NVL | 94 GB HBM3 | PCIe | Hopper | LLM inference / fine-tuning |
| H100 | 80 GB HBM3 | PCIe / SXM5 | Hopper | Training and inference |
| H200 | 141 GB HBM3e | SXM / HGX baseboard | Hopper | Multi-GPU training |
| A100 | 40 / 80 GB HBM2e | PCIe | Ampere | General AI / HPC |
| L40S | 48 GB GDDR6 | PCIe | Ada | Inference, graphics, video |
| L4 | 24 GB GDDR6 | PCIe | Ada | Edge inference, video |
| RTX Pro 6000 | 96 GB GDDR7 | PCIe (Server / Max-Q / Workstation) | Blackwell | Visualization, AI workstations |
DDR5 server memory
| Capacity | Speed | Type | Application |
|---|---|---|---|
| 64 GB | 5600 MT/s | DDR5 ECC RDIMM | AI / enterprise / mission-critical |
| 128 GB | 5600 MT/s | DDR5 ECC RDIMM | AI / mission-critical / database |
| 64 GB | 6400 MT/s | DDR5 ECC RDIMM | AI / new-generation servers |
| 128 GB | 6400 MT/s | DDR5 ECC RDIMM | AI / HPC / mission-critical |
| 32 GB | 5600 / 6400 MT/s | DDR5 ECC RDIMM | General enterprise servers |
| 256 GB | 5600 / 6400 MT/s | DDR5 ECC RDIMM / 3DS | In-memory database, high capacity |
Enterprise NVMe SSDs
| Capacity | Form factor | Interface | Class | Models |
|---|---|---|---|---|
| 3.84 / 7.68 / 15.36 TB | E3.S | PCIe 5.0 x4 NVMe | Enterprise, mission-critical | Samsung PM1753, Micron 9550 PRO, KIOXIA CM7 |
| 3.84 / 7.68 / 15.36 TB | 2.5" U.2 / U.3 | PCIe 5.0 x4 NVMe | Data center | Samsung PM9D3a, Micron 7600 PRO, KIOXIA CD8P |
Transceivers and AI switching
| Speed | Type | Form factor | Reach | Use |
|---|---|---|---|---|
| 400G | SR4 optical transceiver | QSFP112 / OSFP | 50 m (MMF) | ConnectX-7, BlueField-3, AI servers |
| 800G (2x400G) | Twin-port SR8 transceiver | OSFP | 50 m (MMF) | Quantum-2 / Spectrum-4 AI fabric |
| 800G | DR4 optical transceiver | OSFP | 500 m (SMF) | AI fabric interconnect |
| 400G | DR4 optical transceiver | OSFP / QSFP112 | 100 - 500 m (SMF) | AI / data center / HPC |
| NDR 400G x64 | NVIDIA Quantum-2 QM9700 / QM9790 | 1U switch, 51.2 Tb/s | InfiniBand | DGX SuperPOD, large training clusters |
| 400G / 800G | H3C S9855 / S9827 | 1U switch, up to 102.4 Tb/s | RoCE Ethernet | High-density AI data centers |
GPU servers and DGX / HGX systems
| Vendor | Model | GPUs | PCIe | Positioning |
|---|---|---|---|---|
| Supermicro | SYS-422GL-NR | 8x | Gen5 | AI / HPC / enterprise |
| Supermicro | AS-5126GS-TNRT2 | Up to 10 | Gen5 | AI / inference / HPC |
| HPE | ProLiant Compute DL380a Gen12 | Up to 8 | Gen5 | Enterprise AI / inference |
| Dell | PowerEdge XE7745 | 8 | Gen5 | AI / inference / HPC |
| Lenovo | ThinkSystem SR675 V3 | 8 | Gen5 | AI / enterprise |
| NVIDIA | HGX H200 / B200 / B300 / Rubin NVL8 | 8x SXM | NVLink | Large-scale training |
| NVIDIA | DGX H100 / H200 / B200 / B300 | 8x SXM | NVLink | Turnkey AI BasePOD |
HGX platform comparison
| Specification | HGX B200 | HGX B300 | Rubin NVL8 |
|---|---|---|---|
| GPUs | 8x Blackwell SXM | 8x Blackwell Ultra SXM | 8x Rubin SXM |
| Total GPU memory | 1.4 TB | 2.1 TB | 2.3 TB |
| FP8 / FP6 Tensor | 72 PFLOPS | 72 PFLOPS | 140 PFLOPS |
| FP4 Tensor | 144 PFLOPS | 144 PFLOPS | 400 PFLOPS (NVFP4 inference) |
| NVLink | 5th gen, 1.8 TB/s GPU-to-GPU | 5th gen, 1.8 TB/s GPU-to-GPU | 6th gen, 3.6 TB/s GPU-to-GPU |
| Networking bandwidth | 0.8 TB/s | 1.6 TB/s | 1.6 TB/s |
Let's worktogether
Planning an AI deployment? Tell us the workload and we size it, from a single workstation to a full rack.
rehabeldin@gmail.com