Open-source model guide
Which model, and what it takes to run it
Three real open-source models from the arena.ai agent leaderboard — and the honest, no-hand-waving memory math for each one.
What "parameters" means
A model's size is its parameter count — the number of learned weights inside it. The "B" in "1600B" means billions. More parameters generally means a more capable model, but every one of them has to physically fit in GPU memory to run.
Why 1 GB per billion
These models are served at 8-bit precision, which is 1 byte per parameter. So a billion parameters is about a billion bytes — roughly 1 GB. That's the rule of thumb this whole store is built on: parameters (in billions) × 1 GB.
MoE and "active" params
These are Mixture-of-Experts models: only a fraction of the parameters ("active" params) do the work on any given token, which makes them fast. But the whole model still has to be loaded into memory — so memory is sized by total params, not active ones.
The +20% working room
Weights aren't the only thing in memory. The running model also needs room for the conversation it's processing (the KV cache) and scratch space. We add 20% on top of the raw weights so the recommended hardware actually works in practice, not just on paper.
At a glance
All three models side by side, with the minimum memory each one needs to run.
| Model | Total params | Active params | License | Min. memory (weights +20%) | Recommended build |
|---|---|---|---|---|---|
| DeepSeek V4 Pro | 1,600B | 49B | MIT | 1,920 GB | 3× DGX H100 |
| GLM 5.2 Max | 753B | 40B | MIT | 904 GB | 2× DGX H100 |
| Nemotron 3 Ultra | 550B | 55B | OpenMDW-1.1 | 660 GB | 2× DGX H100 |
The math, model by model
DeepSeek V4 Pro
MIT Mixture of ExpertsA 1.6 trillion parameter mixture-of-experts model — only 49B parameters are active per token, but every parameter still has to live in GPU memory.
Minimum setup: Startup Build — DeepSeek V4 Pro
3 DGX H100 systems, networked together, give 1,920GB of pooled GPU memory — exactly enough headroom for DeepSeek V4 Pro's 1,920GB minimum.
GLM 5.2 Max
MIT Mixture of ExpertsA 753B parameter MoE model with 40B active parameters — a strong open model at roughly half the memory footprint of DeepSeek V4 Pro.
Minimum setup: Startup Build — GLM 5.2 Max
2 DGX H100 systems give 1,280GB of pooled memory, comfortably above GLM 5.2 Max's 903.6GB minimum, at the lowest real unit count that clears the bar.
Nemotron 3 Ultra
OpenMDW-1.1 Mixture of ExpertsNVIDIA's 550B parameter MoE model, the smallest of the three but still far beyond what a single GPU — or even a single desktop card cluster — can hold.
Minimum setup: Startup Build — Nemotron 3 Ultra
Nemotron 3 Ultra needs 660GB minimum — just over what a single DGX H100 (640GB) provides, so the cheapest safe build is 2 DGX H100 systems (1,280GB).
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