nvidia / text-generation updated 3 months ago

NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4

:---:--- Total Parameters 120B (12B active) Architecture LatentMoE - Mamba-2 + MoE + Attention hybrid with Multi-Token Prediction (MTP) Context Length Up to 1M tokens Minimum GPU Requirement 1× B200 OR 1× DGX Spark Supported Languages English, French, German, Italian, Japanese, Spanish, Chinese Best For Agentic workflows, long-context reasoning, high-volume workloads (e.g. IT ticket automation), tool use, RAG Reasoni...

Params
67.2 B
Context
262,144
Downloads 30d
2.8 M
Likes
415
Licence: other Not gated SAFETENSORS 7 languages View on Hugging Face ↗

Download history

daily snapshots · 10 days
3.1 M2.8 M
Jul 28Jul 31Aug 3Aug 6

Can you run it?

Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.

FileQuantSizeEst. VRAMVerdict on RTX 4090 · 24 GB
model.safetensors u8 80.3 GB 98.9 GB ❌ Won’t fit
model.safetensors (bf16, full) bf16 + 256K ctx 80.3 GB 411.6 GB ❌ Won’t fit
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.

Run it

copy-paste, exact tags checked against the Hub
~ · curl · api/v1
$ curl -s https://aimodelscomparison.com/api/v1/models/nvidia-nemotron-3-super-120b-a12b-nvfp4
{
  "hf_id": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4",
  "params_b": 67.23,
  "context_length": 262144,
  "license": { "id": "other", "commercial": "unknown" },
  "downloads_30d": 2830012,
  "vram_estimates": [
    { "quant": "u8", "gb": 98.9 }
  ],
  "updated_at": "2026-07-28T17:07:32Z"
}
est. VRAM —on RTX 4090 · 24 GBJSON API →

Specifications

Architecture
NemotronHForCausalLM
Parameters
67.2 B
Tensor type
U8
Context length
262,144
Vocabulary
131,072
Layers / heads
88 / 32
Licence
other
First seen on the Hub
2026-03-10
Training datasets
nvidia/nemotron-post-training-v3, nvidia/nemotron-pre-training-datasets
Added to our catalog
2026-07-28