NVIDIA-Nemotron-3-Super-120B-A12B-FP8
:---:--- 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 2× H100-80GB 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 Reasoning Mode Con...
Params
123.6 B
Context
262,144
Downloads 30d
208 K
Likes
271
Download history
daily snapshots · 10 days225 K208 K
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.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | f8_e4m3 | 128.4 GB | 160.2 GB | ❌ Won’t fit |
| model.safetensors (bf16, full) | bf16 + 256K ctx | 128.4 GB | 735.0 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 -s https://aimodelscomparison.com/api/v1/models/nvidia-nemotron-3-super-120b-a12b-fp8
{
"hf_id": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8",
"params_b": 123.61,
"context_length": 262144,
"license": { "id": "other", "commercial": "unknown" },
"downloads_30d": 208471,
"vram_estimates": [
{ "quant": "f8_e4m3", "gb": 160.2 }
],
"updated_at": "2026-07-28T18:07:39Z"
}
Specifications
- Architecture
- NemotronHForCausalLM
- Parameters
- 123.6 B
- Tensor type
- F8_E4M3
- 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
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