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mradermacher/Llama-3_1-Nemotron-Ultra-253B-v1-GGUF overview

About < quantize version: 2 < output tensor quantised: 1 < convert type: hf < vocab type: < tags: static quants of https://huggingface.co/nvidia/Llama 3 1 Nemo…

transformersnvidiallama-3pytorchenbase_model:nvidia/Llama-3_1-Nemotron-Ultra-253B-v1base_model:finetune:nvidia/Llama-3_1-Nemotron-Ultra-253B-v1license:otherendpoints_compatibleregion:us
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Model Details

Model IDmradermacher/Llama-3_1-Nemotron-Ultra-253B-v1-GGUF
Authormradermacher
Pipeline—
Licenseother
Base modelnvidia/Llama-3_1-Nemotron-Ultra-253B-v1
Last modified2026-10-11T21:04:37.000Z

Model README

---

base_model: nvidia/Llama-3_1-Nemotron-Ultra-253B-v1

language:

  • en

library_name: transformers

license: other

license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/

license_name: nvidia-open-model-license

mradermacher:

readme_rev: 1

quantized_by: mradermacher

tags:

  • nvidia
  • llama-3
  • pytorch

---

About

<!-- ### quantize_version: 2 -->

<!-- ### output_tensor_quantised: 1 -->

<!-- ### convert_type: hf -->

<!-- ### vocab_type: -->

<!-- ### tags: -->

static quants of https://huggingface.co/nvidia/Llama-3_1-Nemotron-Ultra-253B-v1

<!-- provided-files -->

For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3_1-Nemotron-Ultra-253B-v1-i1-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's

READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for

more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

| Link | Type | Size/GB | Notes |

|:-----|:-----|--------:|:------|

| PART 1 PART 2 | Q2_K | 93.5 | |

| PART 1 PART 2 PART 3 | Q3_K_S | 109.8 | |

| PART 1 PART 2 PART 3 | Q3_K_M | 122.0 | lower quality |

| PART 1 PART 2 PART 3 | Q3_K_L | 132.8 | |

| PART 1 PART 2 PART 3 | IQ4_XS | 137.1 | |

| PART 1 PART 2 PART 3 | Q4_K_S | 144.5 | fast, recommended |

| PART 1 PART 2 PART 3 PART 4 | Q4_K_M | 151.0 | fast, recommended |

| PART 1 PART 2 PART 3 PART 4 | Q5_K_S | 174.6 | |

| PART 1 PART 2 PART 3 PART 4 | Q5_K_M | 178.6 | |

| P1 P2 P3 P4 P5 | Q6_K | 208.0 | very good quality |

| P1 P2 P3 P4 P5 P6 | Q8_0 | 269.4 | fast, best quality |

Here is a handy graph by ikawrakow comparing some lower-quality quant

types (lower is better):

!image.png

And here are Artefact2's thoughts on the matter:

https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to

questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting

me use its servers and providing upgrades to my workstation to enable

this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

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