Bayram's AI Hardware Co.

Bayram's AI Hardware Co.

We help you figure out exactly which NVIDIA hardware you need to run big open-source AI models - and we send you a real, honest quote. No payment collected here, no accounts, no upsell. Just the numbers, explained.

How we talk about power

Every watt number on this page is converted into things you can picture, using three simple rules:

Hardware catalog

From a single workstation card to a full datacenter rack. Prices and specs are public estimates as of August 2026.

NVIDIA RTX 6000 Ada Generation

Desktop / Workstation GPU

GPU memory
48 GB (GDDR6 ECC)
48GB can hold one mid-size open model (up to roughly 35-40B parameters) plus room for a conversation. It is not enough on its own for the 100B+ models this store focuses on - you would need to combine several cards.
Power draw
300 W
300 watts is about what a high-end gaming PC draws under full load. It runs on a normal wall outlet and a normal home or office circuit.
≈ 0.25 average homes · 7.2 kWh/day · 0.08 EV batteries/day
Estimated price
$7,350
A single card for a workstation tower. This is the entry point into 'real' AI-capable GPU memory, not a toy graphics card.

NVIDIA RTX PRO 6000 Blackwell (Workstation Edition)

Desktop / Workstation GPU

GPU memory
96 GB (GDDR7 ECC)
96GB is double the older RTX 6000 Ada. One card comfortably holds a ~70-80B model by itself, and 3-4 of them together can hold a 180B+ open model.
Power draw
600 W
600 watts is roughly 2x a high-end gaming PC. One card runs on a normal outlet; running several together needs a real workstation or small server power supply, not a household power strip.
≈ 0.5 average homes · 14.4 kWh/day · 0.16 EV batteries/day
Estimated price
$8,565
Launch MSRP was $8,565 in March 2025; street prices have risen since as demand has outpaced supply. Still far cheaper per GB of memory than datacenter GPUs.

NVIDIA H200

Datacenter GPU

GPU memory
141 GB (HBM3e)
141GB of ultra-fast HBM memory (not regular graphics memory) is enough to hold most 100B-class open models on a single card. A handful together comfortably run 400B-700B class models.
Power draw
700 W
700 watts per card - and H200s are only sold inside servers holding 4 or 8 of them plus heavy-duty datacenter cooling and power delivery. Not something you plug into a wall at home.
≈ 0.58 average homes · 16.8 kWh/day · 0.19 EV batteries/day
Estimated price
$35,000
H200 cards aren't sold individually to the public; this is a typical per-GPU cost when buying (or renting the equivalent of) a datacenter server built around them.

NVIDIA GB300 NVL72 Rack

Full Datacenter Rack (Cluster)

GPU memory
20,736 GB (HBM3e across 72 Blackwell Ultra GPUs)
About 20.7 TB (20,736 GB) of GPU memory spread across 72 GPUs that are wired together closely enough to act like one giant GPU. That's enough to hold the largest open models available today, with room for many simultaneous users.
Power draw
132,000 W
132,000 watts (132 kilowatts), continuously. That's why hardware at this scale lives in purpose-built datacenters with industrial power and liquid cooling - not an office.
≈ 110.0 average homes · 3168.0 kWh/day · 35.2 EV batteries/day
Estimated price
$3,800,000
NVIDIA does not publish official rack pricing. Industry analysts estimate $3.7-4.0 million per rack. This tier is for serious AI companies and cloud providers, not individual startups.

Open models we plan around

Big open-weight models, 100B+ parameters. We size hardware using one honest rule: parameters (in billions) × 1 GB, plus 20% working room for the running conversation. Example: 397B parameters needs 397 GB + 20% = 476.4 GB of GPU memory.

Model Maker License Parameters Memory math GPU memory needed
Falcon 180B

One of the earliest truly open 100B+ models. A good baseline for understanding memory needs at this scale.

 TII (Technology Innovation Institute) Falcon-180B TII License (Apache 2.0-derived, with usage restrictions) 180B total 180 GB × 1.2 216.0 GB
Llama 3.1 405B

Meta's largest openly-downloadable model, and the first open-weight model competitive with closed frontier models.

 Meta Llama 3.1 Community License (free for most commercial use, custom terms) 405B total 405 GB × 1.2 486.0 GB
DeepSeek-V3

A 'Mixture of Experts' model: 671B parameters total, but only about 37B are active per word generated. All 671B still have to fit in GPU memory even though not all of them fire at once - like keeping an entire reference library on the shelf even if you only pull a few books per question.

 DeepSeek AI MIT License (code) + DeepSeek Model License (weights, commercial use allowed) 671B total
(37B active per token) 		671 GB × 1.2 805.2 GB
Kimi K2

Roughly 1 trillion parameters total (32B active per token). Currently one of the largest open-weight models publicly available.

 Moonshot AI Modified MIT License (commercial use allowed, attribution required) 1000B total
(32B active per token) 		1000 GB × 1.2 1,200.0 GB

Recommended builds

Two starting points. Both end in a real, priced, powered build - not a vague suggestion.

Small Startup Build

Small startup — Cheapest honest setup to run one big open model.

Hardware: 3× NVIDIA RTX PRO 6000 Blackwell (Workstation Edition)

Sized to run: Falcon 180B (needs 216.0 GB)

Combined GPU memory
288 GB
That's 72.0 GB more than Falcon 180B needs, for real conversations and multiple users.
Combined power draw
1,800 W
≈ 1.5 average homes · 43.2 kWh/day · 0.48 EV batteries/day
Combined price
$25,695

Falcon 180B needs about 216GB of GPU memory once you add the 20% working room. Three RTX PRO 6000 Blackwell workstation cards give you 288GB combined - enough headroom to run the model plus handle real conversations, without paying for datacenter-only hardware. This is the cheapest setup in our lineup that can honestly run a 100B+ open model, and it fits in a single workstation tower on normal power.

I want this build

Mid-Size Company Build

Mid-size company — More users, more traffic, serious capacity, room to grow.

Hardware: 8× NVIDIA H200

Sized to run: DeepSeek-V3 (needs 805.2 GB)

Combined GPU memory
1,128 GB
That's 322.8 GB more than DeepSeek-V3 needs, for real conversations and multiple users.
Combined power draw
5,600 W
≈ 4.67 average homes · 134.4 kWh/day · 1.49 EV batteries/day
Combined price
$280,000

This is a standard 8-GPU datacenter server - the same GPU count used in NVIDIA's own reference systems. Eight H200 cards give 1,128GB of combined memory: enough to run DeepSeek-V3 (805GB required) with over 300GB of headroom left for serving many users' conversations at once. It draws serious power (5.6 kilowatts) and costs real money ($280,000), but it's what growing companies actually buy to serve production AI traffic instead of a single demo user.

I want this build

What is a cluster?

A cluster is a group of separate computers, or GPUs, wired together closely enough that they work as one team on the same job.

Our biggest product, the NVIDIA GB300 NVL72 Rack, is a cluster in a single rack: 72 GPUs combined into one system.

Combined GPU memory (72 GPUs)
20,736 GB
Combined power draw
132,000 W
≈ 110.0 average homes · 3168.0 kWh/day · 35.2 EV batteries/day
Combined price
$3,800,000

A real cluster vs. a pile of desktop cards - the honest difference

A real datacenter cluster like the GB300 NVL72 connects its 72 GPUs with NVLink and InfiniBand: extremely fast, purpose-built wiring that lets all 72 GPUs share memory and work almost as if they were one enormous GPU, with professional liquid cooling and redundant power feeding every card.

A "pile of desktop cards" - say, several RTX 6000 Ada cards plugged into a regular workstation over standard PCIe slots - can still split a big model across cards and technically run it. But the connections between the cards are far slower, there's no shared-memory fabric, and a home or office power circuit with air cooling was never designed to run flat-out for months. It will work for testing and small-scale use. It is not the same class of machine as a real cluster, and it will not perform or scale anywhere near as well.

Not sure which build fits you?

Tell us what you're trying to run and we'll send you an honest quote - no obligation, no payment collected.