On-Prem AI Hardware for Business: 10, 50, and 200-Seat Tiers

Written by Jakub Rusinowski · Last updated 2026-07-12 · Hardware figures computed by our VRAM engine

Business AI hardware is sized by two numbers: the VRAM the model needs (fixed, computable) and the concurrency your team generates (roughly 5–10% of headcount active at once). That yields three practical tiers: a 24–48 GB GPU workstation for ~10 seats ($2,500–7,000), a single 48–96 GB pro card for ~50 seats ($7,000–17,000), and a 2–8× server-GPU box for 200+ seats ($30,000+). Every VRAM figure below comes from this site's compute engine, so you can verify any row before spending anything.

How to size: VRAM is physics, concurrency is arithmetic

Two quantities determine the tier; neither is a matter of vendor opinion.

VRAM demand is set by the model: weights at your chosen quantization plus KV-cache for active contexts. It's deterministic — our compatibility checker computes it for any model/GPU pair, and every table on this page uses the same engine. If a quote's numbers disagree with the physics, use the vendor checklist.

Concurrency is what headcount actually means for hardware. A 50-person team does not send 50 simultaneous requests: in practice 5–10% of seats are active at any instant, and modern serving stacks (vLLM with continuous batching) interleave those requests on one card with modest latency cost. So the question "what serves 50 people?" usually reduces to "what serves 3–5 concurrent streams of a 70B-class model?" — a single 80–96 GB card, not a rack.

Two adjustments push you up a tier: long contexts (RAG over big documents inflates KV-cache — budget headroom, not just weights) and latency-sensitive workloads (coding autocomplete wants its own small always-loaded model rather than queueing behind chat). When in doubt, rent the exact card for an afternoon and measure — see the rental note below.

Tier 1 — the department workstation (~10 seats)

One tower under a desk. Q4 VRAM figures computed by the site engine:

Hardware: an RTX 4090 (24 GB) build at ~$2,600 covers the 32B class with context headroom; step to an RTX 6000 Ada (48 GB) if you want 70B-class models, ECC memory, and a blower cooler that behaves in an office. Apple's Mac Studio (64–192 GB unified) is the silent-office alternative — slower per token, but zero server-room requirements. Complete part lists at /build.

Tier 2 — one pro card for the company (~50 seats)

A single workstation or 2U server with one 48–96 GB GPU:

Hardware: the RTX PRO 6000 Blackwell (96 GB) is the current sweet spot — one card, both models above, workstation form factor. The NVIDIA L40S (48 GB) is the rack-server equivalent when IT wants it in the datacenter (passive cooling, standard 2U OEM configs from Dell/HPE/Lenovo). Used/refurb A100 80GB is the value play: proven platform, active secondary market, runs a 70B entirely on one card.

Tier 3 — the inference server (200+ seats)

A dedicated 2–8 GPU server; at this tier vLLM serving and batch throughput dominate the design:

Hardware: 2–4× H100 80GB remains the reference configuration — first-class vLLM/TensorRT-LLM support, FP8, and the OEM ecosystem — with 2–4× RTX PRO 6000 Blackwell as the cost-efficient challenger where NVLink isn't required. At this tier, buy through an OEM server configuration rather than assembling cards: power delivery, airflow, and warranty coverage are where self-builds go wrong. And pilot on rented capacity first — a week of rented H100 time costs less than one sizing mistake.

The cards, at current prices

Availability and street prices move — check current listings (datacenter cards are typically bought inside OEM servers; listings below are for the workstation-friendly options):

No hardware? Rent the GPU first

Sizing dispute? Settle it with data: rent the exact GPU class for a few dollars an hour, run your real documents at your real concurrency, then buy what the measurement says:

• RunPod — RTX 4090 ≈ $0.3–0.7/hr, A100/H100 by the hour; serverless per-second billing available
• Vast.ai — Marketplace of hosted GPUs — often the cheapest per hour (interruptible options)
• Lambda — On-demand A100/H100/B200 instances and clusters, per-hour billing

Full list on the cloud AI directory.

The buying checklist

• VRAM first, compute second. A model that doesn't fit doesn't run; a model that fits slowly still works. Err toward more memory.
• Verify every sizing claim against the compatibility checker — including ours.
• Budget the whole box: PSU headroom (a 600W card wants a 1200W+ supply), NVMe for model storage (models are 20–120 GB each), and 64–128 GB system RAM for the serving stack.
• ECC and pro drivers matter at tiers 2–3 — this machine becomes production infrastructure the day the pilot succeeds.
• Plan the refresh, not the purchase: hardware runs 3+ years, but reassess the *model* quarterly — quality per GB keeps improving on the same silicon, which is the hidden dividend of owning the box (TCO details).

Frequently asked questions

Keep going

• Verify any model/GPU pairing with the VRAM engine
• Complete part lists by budget
• The small-team economics behind tier 1
• RTX PRO 6000 Blackwell — full specs and what it runs
• On-Prem LLM Total Cost of Ownership vs Cloud APIs at Company Scale
• On-Prem LLM Deployment Architecture: vLLM, SSO, and Logging
• Enterprise & Sovereign AI — the full hub