AI GPU and Workstation Configuration Solutions
Challenges You Are Facing
- Global GPU shortage: H100 / A100 lead times run from 12 to 40 weeks.
- Difficult model selection: hard to match H100, A100, L40S and RTX 6000 Ada to the right workload.
- Power and cooling design: an 8-GPU H100 rack can easily draw 10kW, making cooling planning difficult.
- CUDA / driver version compatibility: mismatched software stacks and drivers waste time on debugging.
- Blurred budget: a single machine ranges from HKD 500,000 to 3,000,000, so a TCO calculation is needed to support the decision.
- Cloud versus on-premises: monthly cloud compute easily exceeds HKD 200,000, which does not pay off over the long term.
C2 Solution
Workload-driven configurations:
- LLM Training (7B parameters and above): H100 80GB SXM / PCIe clusters, NVLink 900GB/s.
- LLM Inference: L40S / A100 40GB, multi-GPU single machine.
- Fine-tuning (LoRA / QLoRA): RTX 6000 Ada / A100 80GB.
- Computer Vision / Render: RTX 4090 / RTX 6000 Ada.
- HPC / Simulation: H100 NVL, L40S, HPC GPU.
Pre-installed software stack:
- Ubuntu 22.04 LTS / 24.04 LTS
- NVIDIA Driver + CUDA + cuDNN + NCCL
- Docker + NVIDIA Container Toolkit
- PyTorch / TensorFlow (optional pre-load)
Supporting design:
- Power and rack design: working with the data centre to set the power budget and plan in-row cooling and rear-door heat exchangers.
- Cloud versus on-premises TCO analysis: 1 / 3 / 5-year TCO comparison + break-even point calculation.
Who It Suits
| Business Type | Workload | Recommended Configuration |
|---|---|---|
| AI startups | LLM Training / Fine-tuning | 4–8 x H100 PCIe + NVLink bridge |
| Universities / research laboratories | Research, NLP, CV | 2–4 x H100 / A100 |
| Rendering / VFX studios | Rendering, Simulation | 8 x RTX 6000 Ada |
| Financial institutions | Quantitative Modeling | 4 x L40S / A100 |
| Medical imaging companies | Medical AI Inference | 2 x RTX 6000 Ada |
| Autonomous driving / Robotics | Perception Training | 8 x H100 + 100GbE cluster |
Service Process
- Workload interview (model size, batch size, training duration)
- GPU selection + cluster topology design
- Power / cooling / rack planning
- 5-year TCO + cloud-versus-on-premises comparison
- Quotation + lead time confirmation
- Pre-install (OS + Driver + CUDA)
- Data centre installation + cluster network testing
- Workload testing + handover
- Quarterly driver / firmware updates
Supported Brands and Models
| Brand | Key models / GPU | Positioning |
|---|---|---|
| NVIDIA GPU | H100 SXM5 80GB, H100 PCIe, A100 40/80GB, L40S, L4, RTX 6000 Ada, RTX 4090 | Core AI compute |
| Supermicro | SYS-420GP-TNR, AS-4124GS-TNR | Mainstream GPU server |
| Dell | PowerEdge XE9680, XE8640 | 8-GPU enterprise |
| HPE | ProLiant DL380a Gen11, Cray EX | HPC / AI |
| Lenovo | ThinkStation PX, SR675 V3 | Workstation / dual-GPU server |
| HP | Z8 G5, Z4 G5 | Workstation |
| ASUS | ESC8000 / ESC4000 | Multi-GPU server |
| GIGABYTE | G593 / G494 series | Value-for-money GPU server |
Warranty and After-Sales
- 3-year 24x7x4 On-site: standard on GPU servers, on-site within 4 hours.
- 5-year Proactive Care: automated HPE / Dell firmware health monitoring.
- NVLink Bridge warranty: 3 years, the same as the server itself.
- Quarterly driver / firmware updates: optional subscription service.
- Spare GPU pre-stocking: from HKD 50,000 per unit; H100 / A100 / L40S stocked locally in Hong Kong (subject to supply).
FAQ
What is the GPU lead time?
H100 SXM is typically 12–20 weeks; A100 / L40S are faster (6–10 weeks).
How are multiple GPUs interconnected?
4 / 8-GPU fully interconnected NVLink / NVSwitch, 100–200GB/s intra-node.
What is the power requirement?
Each H100 SXM draws 700W; 8 GPUs total about 5.6kW (excluding CPU and storage), and 8–12kW for the whole rack.
Cloud or on-premises?
Projects under 6 months should use the cloud; those over 12 months with utilisation above 60% should go on-premises.
What does the software stack include?
Ubuntu + NVIDIA Driver + CUDA 12.x + Docker + PyTorch (optional pre-install).
How do we choose liquid cooling?
Air cooling is sufficient for up to 4 GPUs; liquid cooling is recommended for 8 GPUs or SXM (cold plate / rear-door).
InfiniBand or Ethernet?
InfiniBand NDR 400Gbps is recommended for training clusters; 100GbE RoCE for inference.
Success Stories
Case A — 8-GPU H100 training cluster for a local AI company
A local large-language-model startup needed to train a 70B model. C2 supplied 1 Supermicro AS-8126GS-TNMR + 8 x H100 SXM5 80GB + 4 x NVLink Switch + InfiniBand NDR, with in-row cooling, a 30kW UPS and 5-year Proactive Care. Delivery: 12-week lead time + 2-week on-site setup. Result: training throughput 3.2x faster than an A100 cluster, with pretraining completed within 3 months.
Case B — University LLM lab workstations
A local university's NLP research group needed 4 workstations for its PhD students. C2 supplied 4 Lenovo ThinkStation PX + 2 x RTX 6000 Ada + 128GB RAM + 4TB NVMe, with a 4-week lead time and pre-loaded Ubuntu, PyTorch and CUDA. Result: students began research the same day and paper submission rates rose by 40%.
Get a Quote
Want an AI GPU quote? Provide your workload details (model size / batch size / training target) for a reply within 48 hours with GPU selection advice and a TCO comparison. PoC and cloud-versus-on-premises break-even analysis are supported, with zero-cost onboarding.
