mradermacher/nvidia-nemotron-3-super-120b-a12b-bf16-gguf overview

{
  "metadata": {},
  "card_data": {
    "base_model": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
    "datasets": [
      "nvidia/nemotron-post-training-v3",
      "nvidia/nemotron-pre-training-datasets"
    ],
    "language": [
      "en",
      "fr",
      "es",
      "it",
      "de",
      "ja",
      "zh"
    ],
    "library_name": "transformers",
    "license": "other",
    "license_link": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/",
    "license_name": "nvidia-nemotron-open-model-license",
    "mradermacher": {
      "readme_rev": 1
    },
    "quantized_by": "mradermacher",
    "tags": [
      "nvidia",
      "pytorch",
      "nemotron-3",
      "latent-moe",
      "mtp"
    ],
    "frontmatter": {
      "base_model": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
      "datasets": [
        "nvidia/nemotron-post-training-v3",
        "nvidia/nemotron-pre-training-datasets"
      ],
      "language": [
        "en",
        "fr",
        "es",
        "it",
        "de",
        "ja",
        "zh"
      ],
      "library_name": "transformers",
      "license": "other",
      "license_link": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/",
      "license_name": "nvidia-nemotron-open-model-license",
      "mradermacher": [],
      "quantized_by": "mradermacher",
      "tags": [
        "nvidia",
        "pytorch",
        "nemotron-3",
        "latent-moe",
        "mtp"
      ]
    },
    "hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
    "summary": "## About         static quants of https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16  ***For a convenient overview and download list, visit our model page for this model.*** weighted/imatrix quants are available at https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nbase_model: nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16\ndatasets:\n- nvidia/nemotron-post-training-v3\n- nvidia/nemotron-pre-training-datasets\nlanguage:\n- en\n- fr\n- es\n- it\n- de\n- ja\n- zh\nlibrary_name: transformers\nlicense: other\nlicense_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/\nlicense_name: nvidia-nemotron-open-model-license\nmradermacher:\n  readme_rev: 1\nquantized_by: mradermacher\ntags:\n- nvidia\n- pytorch\n- nemotron-3\n- latent-moe\n- mtp\n---\n## About\n\n<!-- ### quantize_version: 2 -->\n<!-- ### output_tensor_quantised: 1 -->\n<!-- ### convert_type: hf -->\n<!-- ### vocab_type:  -->\n<!-- ### tags:  -->\n<!-- ### quants:  x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->\n<!-- ### quants_skip:  -->\n<!-- ### skip_mmproj:  -->\nstatic quants of https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16\n\n<!-- provided-files -->\n\n***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#NVIDIA-Nemotron-3-Super-120B-A12B-BF16-GGUF).***\n\nweighted/imatrix quants are available at https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-i1-GGUF\n## Usage\n\nIf you are unsure how to use GGUF files, refer to one of [TheBloke's\nREADMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for\nmore details, including on how to concatenate multi-part files.\n\n## Provided Quants\n\n(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)\n\n| Link | Type | Size/GB | Notes |\n|:-----|:-----|--------:|:------|\n| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-BF16.Q2_K.gguf) | Q2_K | 53.6 |  |\n| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-BF16.Q3_K_S.gguf) | Q3_K_S | 60.0 |  |\n| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-BF16.Q3_K_M.gguf) | Q3_K_M | 67.3 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-BF16.IQ4_XS.gguf) | IQ4_XS | 67.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-BF16.Q3_K_L.gguf) | Q3_K_L | 70.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-BF16.Q4_K_S.gguf) | Q4_K_S | 75.9 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-BF16.Q4_K_M.gguf) | Q4_K_M | 86.2 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-BF16.Q5_K_S.gguf) | Q5_K_S | 86.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-BF16.Q5_K_M.gguf) | Q5_K_M | 95.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-BF16.Q6_K.gguf) | Q6_K | 113.0 | very good quality |\n| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-BF16.Q8_0.gguf) | Q8_0 | 128.6 | fast, best quality |\n\nHere is a handy graph by ikawrakow comparing some lower-quality quant\ntypes (lower is better):\n\n![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)\n\nAnd here are Artefact2's thoughts on the matter:\nhttps://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9\n\n## FAQ / Model Request\n\nSee https://huggingface.co/mradermacher/model_requests for some answers to\nquestions you might have and/or if you want some other model quantized.\n\n## Thanks\n\nI thank my company, [nethype GmbH](https://www.nethype.de/), for letting\nme use its servers and providing upgrades to my workstation to enable\nthis work in my free time.\n\n<!-- end -->\n",
    "related_quantizations": []
  },
  "tags": [
    "transformers",
    "gguf",
    "nvidia",
    "pytorch",
    "nemotron-3",
    "latent-moe",
    "mtp",
    "en",
    "fr",
    "es",
    "it",
    "de",
    "ja",
    "zh",
    "dataset:nvidia/nemotron-post-training-v3",
    "dataset:nvidia/nemotron-pre-training-datasets",
    "base_model:nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
    "base_model:quantized:nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
    "license:other",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
  "likes": 0,
  "downloads": 733,
  "gated": false,
  "private": false,
  "last_modified": "2026-03-21T08:49:51.000Z",
  "created_at": "2026-03-20T18:07:58.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
}

{
  "_id": "69bd8cfed8eb619ac08d621f",
  "id": "mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-GGUF",
  "modelId": "mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-GGUF",
  "sha": "53b0d9a20548ff98fba17e5767421f9ff52d33e2",
  "createdAt": "2026-03-20T18:07:58.000Z",
  "lastModified": "2026-03-21T08:49:51.000Z",
  "author": "mradermacher",
  "downloads": 733,
  "likes": 0,
  "gated": false,
  "private": false,
  "pipeline_tag": "",
  "library_name": "transformers",
  "siblings_count": 13
}