DATACENTER
NVIDIA DGX H100 SuperPOD (4-node cluster)
Four DGX H100 servers linked by high-speed networking and managed as one cluster. This is the shape a mid-size company buys to serve real traffic with headroom to grow, instead of one box running flat out.
Specs, in plain English
- GPU memory 80 GB per GPU × 32 GPUs = 2560 GB total GPU memory is fast on-card RAM that has to hold a model's parameters plus working data while it runs. If a model is bigger than the GPU memory available, it simply will not load.
- Number of GPUs 32 across 4 nodes How many GPUs work together as one system. More GPUs pool their memory into one bigger pool, which is how you fit models too large for a single card — but they need fast links between them to act as one machine.
- CPU 8x Intel Xeon Platinum 8480C across 4 nodes The CPU prepares and feeds data to the GPUs and runs the software around the model. For running big AI models it rarely limits which model you can run — the GPU memory does.
- System RAM 8192 GB System RAM (separate from GPU memory) holds the operating system, the data pipeline, and anything staged before it reaches the GPU. More headroom here means fewer slowdowns under load.
- Power draw 44000 W Power draw is how much electricity the machine pulls when running flat out. It determines your electric bill, and how much cooling and electrical wiring the room needs.
- Price $1,400,000 The purchase price of the hardware itself — it does not include electricity, cooling, networking, or setup labor.
What this means in everyday terms
This machine draws 44000 watts — about like 37 homes running continuously. Left on 24/7 it uses about 1056 kWh a day, which is roughly 11.7 electric-car batteries worth of energy every day.
Homes = watts ÷ 1,200. Daily energy (kWh) = watts × 24 ÷ 1,000. EV batteries/day = daily kWh ÷ 90.