AI Model Advisor
Three real, open-source, MIT-licensed models from the agent leaderboard. For each one, we show the memory math and the cheapest honest hardware build that can run it.
DeepSeek V4 Flash
284 billion parameters · MIT-licensed
Minimum hardware to run it
2 × B200 (192 GB each)
| Combined memory | 384 GB |
|---|---|
| Combined power draw | 2,000 W |
| Total price | $70,000 |
A “cluster” is a group of individual GPUs (or computers) wired together so they can work as a team on the same job.
Real datacenter clustering (NVLink) wires GPUs together so their memory acts as one shared pool — but just plugging several desktop cards into one PC does NOT do this: each card keeps its own separate memory, and they never merge into a bigger pool.
GLM-5.2
750 billion parameters · MIT-licensed
Minimum hardware to run it
5 × B200 (192 GB each)
| Combined memory | 960 GB |
|---|---|
| Combined power draw | 5,000 W |
| Total price | $175,000 |
A “cluster” is a group of individual GPUs (or computers) wired together so they can work as a team on the same job.
Real datacenter clustering (NVLink) wires GPUs together so their memory acts as one shared pool — but just plugging several desktop cards into one PC does NOT do this: each card keeps its own separate memory, and they never merge into a bigger pool.
DeepSeek V4 Pro
1,600 billion parameters · MIT-licensed
Minimum hardware to run it
7 × B300 / Blackwell Ultra (288 GB each)
| Combined memory | 2,016 GB |
|---|---|
| Combined power draw | 9,800 W |
| Total price | $350,000 |
A “cluster” is a group of individual GPUs (or computers) wired together so they can work as a team on the same job.
Real datacenter clustering (NVLink) wires GPUs together so their memory acts as one shared pool — but just plugging several desktop cards into one PC does NOT do this: each card keeps its own separate memory, and they never merge into a bigger pool.