Real Hardware. Real Specs. No Cloud Markups.

Own the GPUs that run
the world's best AI models.

From a $2,400 desktop card to a $6.5M datacenter rack — every product explained in plain words, with the memory math visible. We'll guide you to exactly what you need.

4Products
3Open-Source Models
2Guided Paths
$0Cloud Bills

Two kinds of customers walk in.

Both leave with a clear path and a price.

🚀

The Small Startup

You want to run one big open-source model without paying cloud GPU bills every month. You're cost-sensitive. You need something that actually works, not enterprise overhead.

Recommended: 8× H200 HGX Server — $370,000
Runs all three models on our list. You own it outright.
See the full path →
🏢

The Mid-Size Company

You need serious capacity — multiple models, multiple teams, room to grow. Downtime costs you money. You're thinking in years, not months.

Recommended: GB300 NVL72 Rack — from $3.7M
72 GPUs, 20 TB pooled memory. Enterprise-grade clustering.
See the full path →

The Hardware

Real NVIDIA products. Real specs. Prices you can actually plan around.

The Models You Can Run

Three top-ranked open-source models with MIT or Apache 2.0 licenses — and the hardware each one needs.

Z.ai (zai-org) MIT

GLM-5.2 (Max)

753 B × 1 GB × 1.2 = 904 GB

One of the largest open-source language models available, ranked #10 on the Arena Agent leaderboard.

See minimum hardware →
Tencent Apache 2.0

Tencent Hy3

295 B × 1 GB × 1.2 = 354 GB

Tencent's open frontier model.

See minimum hardware →
DeepSeek MIT

DeepSeek V4-Flash

284 B × 1 GB × 1.2 = 341 GB

DeepSeek's efficient frontier model.

See minimum hardware →

Power: What these numbers actually mean

An average home draws about 1,200 W continuously. A typical EV battery holds about 90 kWh. Here's where our hardware lands.

🖥️

RTX 5090

575 W

= 0.48× average homes continuously

Drains a 90 kWh EV in ~156.5 hours
🔧

RTX PRO 6000

600 W

= 0.50× average homes continuously

Drains a 90 kWh EV in ~150.0 hours
🏭

H200 HGX Server

5,600 W

= 4.7× average homes continuously

Drains a 90 kWh EV in ~16.1 hours

GB300 NVL72 Rack

137,000 W

= 114× average homes continuously

Drains a 90 kWh EV in ~39 minutes

Clusters: What they are and why it matters

What is a cluster? When a model is too large to fit in one machine's memory, you link multiple machines together so they share the work. A cluster is two or more GPU systems cooperating to run a single model or serve many users simultaneously.
✅ Real Datacenter Clustering

NVLink Domain (e.g. GB300 NVL72)

  • All 72 GPUs share one memory address space at the hardware level
  • GPU-to-GPU bandwidth: ~900 GB/s
  • The model sees one giant GPU — no software coordination tricks
  • Low latency, maximum throughput
⚠️ "Naive" Multi-PC Setup

Multiple Desktop Cards (e.g. 4× RTX 5090)

  • Each card has its own isolated memory — they cannot natively share
  • Requires software (tensor parallelism) to coordinate
  • Network bandwidth between PCs: ~25 GB/s (36× slower)
  • Works for training, but adds latency for inference
Example: The GB300 NVL72 at $3.7M combines 72 GPUs into one coherent machine. Buying 72 RTX 5090s at $2,400 each costs $172,800 — but gives you 72 isolated 32 GB islands, not one 2,304 GB pool. For running a 753B parameter model, only the real NVLink domain works reliably.

Ready to stop renting and start owning?

Tell us what you want to build. No payment, no accounts — just a quote request we'll follow up on.