Nemotron 3 Super 120B-A12B — VRAM, Speed & Local Setup

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 15 sierpnia 2026

Model libraryNemotron 3 Super → Nemotron 3 Super 120B-A12B

120B total / 12B active MoE. The low active-parameter count means throughput closer to a 12B model than a 120B one, but the full weight set still has to be resident — roughly 65 GB at Q4_K_M, so dual RTX 3090/4090 or a single 80GB datacenter card. Q8_0 and BF16 tags are published for workstations with more headroom. NVIDIA releases open training data and recipes with this family, which is unusual among open-weight releases.

Nemotron 3 Super 120B-A12B needs about 73 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters120 Billion (12B active)
Context window128,000
ArchitectureMixture-of-Experts (reasoning-trace)
ProviderNVIDIA
LicenceNVIDIA Open Model License
Specified atQ4_K_M
System RAM128 GB
Record updated2026-08-15

Licence

NVIDIA Open Model Licensecommercial use permitted. Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), with no KV cache (this record has no published architecture). Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantWeightsVRAM neededEst. speedFit on 24 GB
Q2_K39.4 GB40.2 GB~16 tok/s (est.)Offloads to system RAM (slow)
Q3_K_M51.2 GB52.0 GB~14 tok/s (est.)Offloads to system RAM (slow)
Q4_K_M72.5 GB73.3 GBWon't fit
Q5_K_M85.0 GB85.8 GBWon't fit
Q6_K98.4 GB99.2 GBWon't fit
Q8_0127.5 GB128.3 GBWon't fit
F16240.0 GB240.8 GBWon't fit

Want the memory numbers alone, at every quantization level and your own context length? Use the Nemotron 3 Super 120B-A12B VRAM calculator.

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Recommended GPU

The cheapest catalogued GPU that runs Nemotron 3 Super 120B-A12B is the AMD Ryzen AI Max+ 395 (96 GB).

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Ryzen AI Max+ 395 Laptop (Strix Halo, up to 128GB)
96 GB VRAM · 120 W board power
Ceny w 2026 są niestabilne — sprawdź aktualną ofertę.
Sprawdź cenę na Amazon

How to Run Nemotron 3 Super 120B-A12B

Install Ollama, then run:

ollama run nemotron-3-super:120b-a12b

Weights on Hugging Face: nvidia/Nemotron-3-Super-120B-A12B.

Best for: agentic tasks, reasoning, enterprise, on premise, multi agent.

Can I Run Nemotron 3 Super 120B-A12B on My GPU?

Nemotron 3 Super 120B-A12B — Frequently Asked Questions

How much VRAM does Nemotron 3 Super 120B-A12B need?
About 73 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does Nemotron 3 Super 120B-A12B run on an RTX 4090 (24 GB)?
No. Nemotron 3 Super 120B-A12B needs about 73 GB at Q4_K_M, more than a single RTX 4090's 24 GB. It needs a larger card, several GPUs, or Apple Silicon with enough unified memory — or it runs with part of the weights offloaded to system RAM, which is much slower.
How do I run Nemotron 3 Super 120B-A12B locally?
Install Ollama and run `ollama run nemotron-3-super:120b-a12b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

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