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Compare / Workstation vs Server vs Rack-Scale

Pick the right class before drilling into specs.

Four classes of on-prem AI system, four very different operating envelopes. The most common mistake is picking by GPU spec sheet rather than by deployment shape. This guide goes the other way.

Class comparison

The four classes, side by side.

Personal · Desk-side

Individual devs, researchers, small teams who want a real NVIDIA stack on the desk.

Users
1–5 concurrent
Model
Up to ~70B with quantization
Form
Compact desktop or desk-side tower
Cooling
Office air — no special HVAC
Power
240 W (Spark) – 1.6 kW (Station)
Budget
RM 16K – RM 500K
Step up
Concurrency >5 users, multi-GPU fine-tunes, or models exceed 128 GB unified memory.

Workstations

Department-level AI — 25–100 concurrent users — without going to a server room.

Users
25–100 concurrent
Model
7B–70B inference; LoRA / QLoRA fine-tune
Form
Pedestal tower (quiet)
Cooling
Hybrid liquid CPU + tuned air
Power
1.6–2 kW on a standard wall outlet
Budget
RM 35K – RM 110K
Step up
>200 users, NVLink-coherent multi-GPU, or 70B+ full fine-tunes.

AI Server

Production org-wide AI on single-node rackmount platforms — EMARQUE AI Server (RTX PRO 6000 SE) and NVIDIA DGX B200 / B300 with HGX OEM alternatives.

Users
200–2,000 concurrent
Model
70B+ inference, full fine-tunes, frontier reasoning
Form
4U–10U rackmount
Cooling
Air-cooled (mostly); raised-floor or in-row preferred
Power
6–14 kW per node, 3-phase recommended
Budget
Reseller-channel pricing — quoted at scoping
Step up
Unit of consumption shifts from nodes to racks (multi-rack NVLink Switch fabric).

AI Factory · rack-scale

Foundation-model labs, sovereign-compute, AI Factories — rack is the unit of consumption. GB300 NVL72, DGX GB300, Vera Rubin NVL72.

Users
Multi-tenant at scale; trillion-parameter training
Model
Trillion-parameter training and reasoning
Form
Single rack as one accelerator (NVL72)
Cooling
Direct-to-chip liquid, CDU integration required
Power
≈ 120 kW per rack
Budget
Contact — multi-rack project
Step up
Add more racks; refresh into Vera Rubin NVL72 generation.
Recommended systems

What we put in front of clients in each class.

AI Server

Production org-wide AI on single-node rackmount platforms — EMARQUE AI Server (RTX PRO 6000 SE) and NVIDIA DGX B200 / B300 with HGX OEM alternatives.

AI Factory · rack-scale

Foundation-model labs, sovereign-compute, AI Factories — rack is the unit of consumption. GB300 NVL72, DGX GB300, Vera Rubin NVL72.

Still on the fence?

The wrong class costs more than the wrong vendor.

Get an architecture call before you commit. We pressure-test the workload assumptions, deployment site, and procurement window — usually within one business day.

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Get in Touch with Us

Tell us about your workload. We reply within one business day with a quote sized to fit.

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    Key Account Manager

    +6012 627 2280
  2. 02

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