Nuraiym's AI Hardware Store

Real NVIDIA hardware -- from desktop cards to full datacenter racks.

Product Catalog

Prices and power draw are approximate, based on publicly reported specs -- actual pricing varies by configuration, region, and negotiation.

ProductPricePower Draw
NVIDIA RTX 4090
Desktop GPU
$1,599
About the price of a high-end gaming laptop.
450 W
About the same draw as 4 hair dryers running at once.
NVIDIA RTX 6000 Ada
Workstation GPU (48GB)
$6,800
About the price of a used sedan.
300 W
About the same as running 3 hair dryers at once.
NVIDIA L40S
Datacenter GPU (48GB)
$11,000
About the price of a brand-new economy car.
350 W
Enough power to run a home oven continuously.
NVIDIA A100 80GB
Datacenter GPU
$10,000
About the price of a small new car.
300 W
Similar to running central air conditioning nonstop.
NVIDIA H100 80GB SXM
Datacenter GPU
$30,000
About the price of a luxury car.
700 W
More than half the power draw of an entire average home.
NVIDIA H200 141GB
Datacenter GPU
$35,000
About the price of a top-trim luxury car.
700 W
Same as H100 -- more than half an average home's power draw.
NVIDIA DGX H100
Complete AI server (8x H100 inside)
$300,000
About the price of a house in many US cities.
10,200 W
≈ 8.5 average homes' worth of power, running around the clock.
NVIDIA GB200 NVL72 Rack
Full datacenter rack (72 GPUs)
$6,500,000
About the price of a private jet.
120,000 W
≈ 100 average homes' worth of power draw, all at once.

Open-Source AI Models (100B+ parameters)

Our rule of thumb for minimum GPU memory: parameters (in billions) × 1GB, plus 20% headroom for the model to actually run.

ModelParametersMin. Memory NeededMinimum Hardware
Llama 3.1 405B
Meta
Meta's largest open-weight model -- a strong general-purpose reasoner and coder.
405B
486.0 GB
405B × 1GB × 1.2 (20% extra)
7x H100 80GB
or 1x DGX H100 system(s)
DeepSeek-V3
DeepSeek AI
A mixture-of-experts model with 671B total parameters, competitive with top closed models.
671B
805.2 GB
671B × 1GB × 1.2 (20% extra)
11x H100 80GB
or 2x DGX H100 system(s)
Falcon 180B
Technology Innovation Institute (TII)
One of the largest fully open-license models, built for research and commercial use.
180B
216.0 GB
180B × 1GB × 1.2 (20% extra)
3x H100 80GB
or 1x DGX H100 system(s)

Customer Path: Small AI Startup

You're a small team building a chatbot or fine-tuning smaller open models. You need real GPU power without a datacenter budget.

Recommended Build: 4x NVIDIA RTX 4090 workstation

4x NVIDIA RTX 4090 $6,396
Total Price
$6,396
Total GPU Memory
96 GB
Total Power
1,800 W
In Homes
1.5 homes
EV Charges / Day
0.48 EVs

Power draw of 1,800 watts is like running 1.5 average homes at once (1 home ≈ 1200 watts). Run continuously for a day, that's 43.2 kWh -- about 0.48 full EV battery charges (1 EV charge ≈ 90 kWh).

Customer Path: Mid-Size Company

You're running a production AI product -- for example a support assistant powered by a 70B-180B parameter model -- serving real customer traffic every day.

Recommended Build: 1x NVIDIA DGX H100 system (8x H100 GPUs)

1x NVIDIA DGX H100 $300,000
Total Price
$300,000
Total GPU Memory
640 GB
Total Power
10,200 W
In Homes
8.5 homes
EV Charges / Day
2.72 EVs

Power draw of 10,200 watts is like running 8.5 average homes at once (1 home ≈ 1200 watts). Run continuously for a day, that's 244.8 kWh -- about 2.72 full EV battery charges (1 EV charge ≈ 90 kWh).

What's a Cluster?

A cluster is just a group of computers (or GPUs) wired together and working as a team, so a job too big for one machine gets split up and finished faster. When you stack multiple GPUs into one build, their memory, price, and power add up -- our Startup build's 4x RTX 4090 combine for 96GB of GPU memory, and the Mid-Size DGX H100 combines for 640GB, both shown above as "Total GPU Memory." That said, a handful of desktop cards stacked in one box is not the same thing as a real datacenter cluster: a true cluster spans many separate physical machines networked together with fast interconnects, redundant power, and cooling built for it -- not just several GPUs sharing one motherboard.

Get a Quote

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