AI Server GPU Comparison: RTX PRO 6000, 5090, A100 & H100
The GPU is the most important and most expensive part of an AI server, and the choice comes down to honest numbers, not hype. Here is how the cards we actually build with compare — how much VRAM each has, what it draws, roughly what it costs, and where it is the right call or the wrong one. Every figure below is a range to re-verify at quote time; prices and benchmarks move.
The one spec that decides everything — VRAM
Start here: more VRAM means a bigger model on one card. The video memory on the GPU holds the model while it runs, so it sets the largest model the card can run at all. A 70B model at Q4_K_M needs roughly 38–40GB; the same model at full FP16 is about 140GB. That single number — how much VRAM you have — decides more about your server than clock speeds or core counts ever will.
Not sure what your model needs? Our VRAM calculator maps model size and quantization to the card that fits.
The contenders at a glance
VRAM, approximate power draw, a price range to verify at quote, and where each card belongs. Used-card prices are volatile — re-check the week you buy.
| GPU | VRAM | ~Power (W) | ~Price range | Best for |
|---|---|---|---|---|
| RTX PRO 6000 Blackwell | 96GB | ~600W | ~$8,000–9,200 | One-card 70B inference; the SMB sweet spot |
| RTX 5090 | 32GB | ~575W | consumer-tier | Smaller models (8B–32B), tight budgets |
| RTX 4090 | 24GB | ~450W | consumer-tier | Entry builds, 8B–13B-class models |
| A100 80GB (used) | 80GB | ~300–400W | volatile, verify | Multi-GPU/NVLink work if condition checks out |
| H100 80GB | 80GB | ~350–700W | ~$25,000–40,000 | Large-model training, dense multi-GPU |
Figures are 2025–2026 ranges and must be re-verified at quote. Power is approximate full-load draw (TGP/TDP); used A100 and H100 pricing is especially volatile.
RTX PRO 6000 Blackwell 96GB — the small-business sweet spot
For most small businesses, one 96GB card beats juggling several smaller ones. With 96GB of VRAM on a single GPU, the RTX PRO 6000 Blackwell runs a 70B-class model at Q4 (~38–40GB) with plenty left over for the KV cache that grows as more people use it at once — no splitting the model across cards, no NVLink, no multi-GPU complexity. It draws around 600W at full load and lands roughly in the $8,000–9,200 street range (verify at quote).
The honest headline: it is competitive on single-GPU inference at a fraction of the price of an H100. That single-card simplicity is why we reach for it first on a team build. To turn the spec into a real machine, see our GPU AI servers and custom AI server builds.
When a consumer card (5090 / 4090) is enough
If you are running smaller models on a tight budget, a consumer card does the job. An RTX 5090 brings 32GB of VRAM; an RTX 4090 has 24GB. Both run 8B-class models very comfortably and reach into the 32B range, which covers a lot of real small-business work — chat, drafting, document search.
The ceiling is VRAM. A 70B model will not fit on one consumer card, full stop. So the question is simple: if the biggest model you want stays under roughly 30GB, a 5090 or 4090 is a sensible, cheaper choice. The moment you want the bigger models, you are looking at a 96GB card or multiple GPUs. For a single-user desk machine rather than a shared server, an NVIDIA AI workstation may be the better fit.
When you actually need an A100 or H100
Data-center cards exist for a reason, but it is an honest one: large-model training and dense multi-GPU serving. Where they pull ahead is the interconnect. NVLink links GPUs at up to roughly 900GB/s, which matters when you split one very large model across many cards with tensor parallelism, or run 8-way setups. That is real infrastructure for real scale.
For most small businesses, that scale never arrives. A single 96GB card handles team inference without needing the model split at all, so the NVLink advantage simply does not come into play. We will tell you plainly when an A100 or H100 earns its keep — and it is rare for an SMB. If you are at that scale, our AI server installation team plans the power and cooling those cards demand.
New vs used data-center GPUs — the real tradeoffs
A used A100 80GB can look like a bargain, and sometimes it is. But the used-data-center market is highly volatile — pricing swings widely by SKU, age, and condition, so a number you see one week can be stale the next. You also buy without a warranty and with unknown wear from a previous workload.
Then there is the physical side. Data-center cards are built for data-center power and airflow — they assume strong front-to-back cooling and dedicated circuits, not a tower under a desk. That is real cost and planning on top of the sticker price. When the savings are genuine and the condition checks out, a used card can make sense; when they are not, a new pro card with a warranty is the safer build. We weigh that for each project rather than assuming the cheaper sticker wins.
Workstation card vs Max-Q vs Server Edition
The RTX PRO 6000 Blackwell comes in a few flavors, and they differ mostly by power and cooling — which decides whether they fit a tower or a rack:
- Workstation (~600W blower) — the full-power card for a single-GPU or light multi-GPU tower with good airflow.
- Max-Q (~300W blower) — power-capped for dense 2–4 GPU workstations where you need several cards in one chassis without cooking them.
- Server Edition (passive) — no onboard fan; it relies on a rack's front-to-back airflow to cool it, for rackmount builds.
Which one is right depends on your chassis and how many cards you are running. We match the variant to the build — see GPU AI servers to configure it.
Pick your GPU in 5 questions
Run through these and the right card usually picks itself.
1. How big is the biggest model you want to run?
Map it to VRAM first. Under ~30GB at Q4 fits a consumer card; a 70B model at ~38–40GB wants a 96GB card.
2. How many people will use it at once?
Concurrency eats VRAM — each active session adds KV cache. More simultaneous users pushes you toward the 96GB card.
3. Are you training, or just running models?
Inference fits one card for most teams. Large-model training or tensor parallelism is where A100/H100 and NVLink earn their place.
4. What is your budget — and risk tolerance?
A used A100 can cut cost but the market is volatile and there is no warranty. New pro cards cost more and carry less risk.
5. Tower or rack, and what power do you have?
A 600W card needs a normal circuit and airflow; multi-GPU and passive Server Edition cards need rack cooling and dedicated power.
We spec the GPU and build it here in Texas
You do not have to settle the RTX PRO 6000 vs A100 vs H100 question alone. We pick the card for your real workload, hand-build the server, and install it on-site across Houston, Katy, Fulshear and the Fort Bend area — then stay on call. See our Texas service areas.
GPU comparison questions
What is the best GPU for a small-business AI server?+
For most small businesses, one RTX PRO 6000 Blackwell with 96GB is the sweet spot — a single card runs a 70B-class model at Q4 with room to spare, for roughly $8,000–9,200 (verify at quote). Consumer cards like the RTX 5090 are cheaper but cap out around 32GB, and data-center cards like the H100 are rarely worth it for a small team.
Can one RTX PRO 6000 Blackwell run a 70B model?+
Yes. A 70B model quantized to Q4_K_M needs roughly 38–40GB of VRAM, so a single 96GB card has plenty of headroom for the weights plus the KV cache that grows as more people use it at once.
Is an RTX 5090 or 4090 enough for an AI server?+
For smaller models and a tight budget, yes — an RTX 5090 (32GB) or RTX 4090 (24GB) runs 8B to 32B-class models well. The limit is VRAM: a 70B model will not fit on one consumer card, so the moment you want the bigger models you are looking at a 96GB card or multiple GPUs.
Is a used A100 worth it for an AI server?+
Sometimes. A used A100 80GB can be cheaper than new pro hardware, but the used-data-center market is highly volatile — pricing swings widely by SKU and condition. You also inherit data-center power and cooling demands and no warranty, so we weigh those tradeoffs against a new card before recommending it.
Do I really need an H100?+
Rarely, for a small business. The H100 earns its keep on large-model training and dense multi-GPU work that uses NVLink for tensor parallelism. For private inference on a team server, a single 96GB card is competitive on single-GPU inference at a fraction of the price.
Next, size your model with the VRAM calculator, or read more on custom AI servers on the main site.
Not sure which GPU your build needs?
Tell us the models you want to run and how many people will use them — we'll pick the right card and build a server you own outright.