Buyer's guide · NVIDIA hardware
NVIDIA H200 vs B200 vs B300 vs DGX Station: which on-premise AI system fits?
Choosing between NVIDIA systems starts with your workload, not the newest part number. This guide puts the four configurations we deliver side by side and explains how to choose between them.
Tiada Solutions · Updated
Side by side
| Specification | Clastiq Station | Clastiq H200 | Clastiq B200 | Clastiq B300 |
|---|---|---|---|---|
| NVIDIA system | NVIDIA DGX Station · GB300 | NVIDIA HGX H200 · 8 GPU | NVIDIA HGX B200 · 8 GPU | NVIDIA HGX B300 · 8 GPU |
| GPU / compute | 1 × NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip | 8 × NVIDIA H200 SXM, 141 GB each, NVLink | 8 × NVIDIA B200 SXM, 180 GB each, NVLink | 8 × NVIDIA B300 SXM, 288 GB each, NVLink |
| GPU memory | 784 GB unified, 288 GB of it HBM3e on the GPU | 1.1 TB total HBM3e | 1.4 TB total HBM3e | 2.3 TB total |
| CPU and system memory | 72-core NVIDIA Grace | Dual-socket, 192 cores · 2 TB DDR5 ECC | Dual-socket, 192 cores · 2 TB DDR5 ECC | Dual-socket, 192 cores · 2 TB DDR5 ECC |
| Serves | Up to 200B-class models in fp4, one GPU shared by fractions | 70B-class models at 128k context, plus fine-tuning | Frontier open models, full fine-tuning of 70B-class | Frontier open models at long context, several at once |
| Storage | NVMe model and dataset store | 30 TB NVMe model and dataset store | 60 TB NVMe model and dataset store | 60 TB NVMe model and dataset store |
| Network | NVIDIA ConnectX-8 SuperNIC, up to 800 Gb/s | 2 × 200 GbE, 1 × management | 2 × 400 GbE, 1 × management | 2 × 400 GbE, 1 × management |
| Power (reference) | Deskside unit on standard office power | 10.2 kW max (NVIDIA DGX H200 reference) | 14.3 kW max (NVIDIA DGX B200 reference) | 15 kW system max (NVIDIA DGX B300 reference) |
| Best for | Departmental copilots, Sovereign AI pilots, Data-science teams, Edge & branch AI | Enterprise LLM gateway, RAG at scale, LoRA / QLoRA fine-tuning, Multi-team AI platform | Frontier open models, Full fine-tuning, National / sovereign AI, High-QPS inference | Frontier open models, Long-context inference, Concurrent large models |
| Full brief | Clastiq Station details | Clastiq H200 details | Clastiq B200 details | Clastiq B300 details |
Configuration figures come from our solution briefs and are confirmed during sizing. Power figures are NVIDIA's published maximums for its reference DGX systems; HGX-based servers vary by chassis.
Start with memory, then workload
GPU memory decides which models fit, how much context you can serve and how many people can use the system at once. Compute decides how fast answers arrive and how quickly fine-tuning finishes. Most buyers should first decide which models they need to run and for how many users, then pick the smallest system that serves them with headroom.
- DGX Station GB300: departmental copilots, sovereign AI pilots, data-science teams and edge or branch AI, on standard office power.
- HGX H200: an organisation-wide LLM gateway, retrieval-augmented generation at scale, LoRA and QLoRA fine-tuning, and a shared multi-team AI platform.
- HGX B200: frontier open models, full fine-tuning of 70B-class models and high-throughput inference.
- HGX B300: frontier open models at long context and several large models served at once, with 288 GB per GPU.
Hopper, Blackwell and Blackwell Ultra
The H200 is a Hopper-generation GPU with 141 GB of HBM3e and FP8 support. The B200 is a Blackwell-generation GPU and the B300 is Blackwell Ultra; both add hardware support for 4-bit floating point (FP4) inference, which lets larger models fit and serve faster in the same memory. The GB300 superchip in DGX Station pairs a Blackwell Ultra GPU with a 72-core NVIDIA Grace CPU in one deskside system.
Newer is not automatically better for every buyer. If your models fit comfortably on H200 and your facility is planned around it, it remains a strong choice for an organisation-wide AI service.
HGX and DGX: what is the difference?
NVIDIA HGX is the 8-GPU baseboard that server makers build complete servers around. NVIDIA DGX is NVIDIA's own complete system. Our 8-GPU H200, B200 and B300 configurations are NVIDIA HGX-based servers, with the chassis selected at quote time. DGX Station is a complete NVIDIA deskside system.
Facility and power planning
8-GPU systems need data centre power and cooling. NVIDIA lists 10.2 kW maximum for DGX H200, 14.3 kW for DGX B200 and 15 kW for DGX B300 as reference points. HGX-based servers vary by chassis, so we confirm the exact figure, rack and cooling requirements during sizing. DGX Station runs on standard office power.
Lead times and export licensing
Delivery timing depends on NVIDIA allocation and, for advanced GPUs, export licensing that applies to the end customer and site. We confirm both during quoting rather than promising a fixed date in advance.
Frequently asked questions
Related solution brief
Clastiq Orchestrator
Turnkey AI platform: Kubernetes, storage, VMs, fractional GPU sharing and an OpenAI-compatible inference gateway on NVIDIA GB300, H200 and B200.
View solution brief detailsSources
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