Model reference · open weights

NVIDIA-Nemotron-3-Super-UD-Q4_K_XL-MTPv2-layers

LLMs meshllm Text gen · MoE 1 build Its own licence terms 5k dl/mo

NVIDIA-Nemotron-3-Super-UD-Q4_K_XL-MTPv2-layers is an open-weight language model from meshllm. NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-MTPv2-layers (BF16) weighs 90.4 GB; the smallest configuration that runs it is 4× RTX 4090 24 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • NVIDIA-Nemotron-3-Super-UD-Q4_K_XL-MTPv2-layers is a GGUF layer package by meshllm designed for text generation across a local Mesh LLM cluster.
  • It splits the 120B-A12B model into 89 per-layer artifacts to enable distributed inference on multiple machines.
  • The package uses UD-Q4_K_XL quantization and is licensed under an other licence.

Summary of the meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-MTPv2-layers model card, 2026-10-01

What it is

Released bymeshllm
Released2026-09-12
VRAM90.4 GB for the weights

What it runs on

Memory and cards for NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-MTPv2-layers (BF16)

90.4 GBweights, file size
651 MBruntime overhead, at least

How much memory each request adds isn't estimated yet for this architecture. The weights need at least the cards below, plus room for the context.

CardWeights alone
RTX 3060 12 GB … H100 80 GB
8 smaller cards
does not fit
RTX PRO 6000 Blackwell 96 GBtight
DGX Spark (GB10) 128 GB unifiedfits
H200 141 GBfits
B200 180 GBfits
4× RTX 4090 24 GB
split by layers (llama.cpp)
tight
4× RTX 3090 24 GB
split by layers (llama.cpp)
tight
4× RTX 5090 32 GB
split by layers (llama.cpp)
fits
2× H100 80 GB
split by layers (llama.cpp)
fits
2× A100 80 GB
split by layers (llama.cpp)
fits
4× L40S 48 GB
split by layers (llama.cpp)
fits
2× RTX PRO 6000 Blackwell 96 GB
split by layers (llama.cpp)
fits

From the model card

What meshllm says about NVIDIA-Nemotron-3-Super-UD-Q4_K_XL-MTPv2-layers

Read the model card

GGUF layer package for running NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-MTPv2 across a local Mesh LLM cluster.

This package is derived from meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-MTPv2-GGUF and keeps the original GGUF distribution split into per-layer artifacts for distributed inference.

Highlights

Run locallyPool multiple machinesOpenAI-compatiblePackage variant
Private inference on your hardwareSplit layers across peersServe /v1/chat/completions locallyUD-Q4_K_XL layer package

Model Overview

PropertyValue
Source modelmeshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-MTPv2-GGUF
Model idmeshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-MTPv2-GGUF:UD-Q4_K_XL-MTPv2
FamilyNVIDIA
Parameter scale120B-A12B
QuantizationUD-Q4_K_XL
Layer count89
Activation widthnot recorded
Package size0 B
Source fileNVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-MTPv2-00001-of-00003.gguf
Package repomeshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-MTPv2-layers
Licenseother from meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-MTPv2-GGUF

Recommended Use

  • Local and private inference with Mesh LLM.
  • Multi-machine serving when the full GGUF is too large for one host.
  • OpenAI-compatible chat/completions workflows through Mesh LLM's local API.

For upstream architecture details, chat template guidance, sampling recommendations, license terms, and benchmark notes, see the source model card: meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-MTPv2-GGUF.

Quickstart

# Run this on each machine that should contribute memory/compute.
mesh-llm serve --model "meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-MTPv2-layers" --split
# Check the mesh and discover the OpenAI-compatible model name.
curl -s http://localhost:3131/api/status
curl -s http://localhost:3131/v1/models
# Send an OpenAI-compatible chat request.
curl -s http://localhost:3131/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-MTPv2-GGUF:UD-Q4_K_XL-MTPv2",
    "messages": [{"role": "user", "content": "Write a tiny hello-world function in Rust."}],
    "max_tokens": 128
  }'

Package Variant

PropertyValue
Formatgguf
Canonical source refmeshllm/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-MTPv2-GGUF@360a2016f3b898925d7dc460d717852ba942948e/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-MTPv2-00001-of-00003.gguf
Source revision360a2016f3b898925d7dc460d717852ba942948e
Source SHA-256e0868d034ab4d3b56a74cfa9786894719c147f851149fe256b79113fe4245bc2
Skippy ABInot recorded
Package manifest SHA-25629c3756b4cc31800322a45dcf691303ec37096930ee69a1f395616b56b2cd69d

What Is Included

ArtifactPathContentsSHA-256
Manifestmodel-package.jsonPackage schema, source identity, checksums29c3756b4cc31800322a45dcf691303ec37096930ee69a1f395616b56b2cd69d

Validation

Generated by the Mesh LLM HF Jobs splitter from mesh-llm ref f932c4d1dc12b3e3a670d5f470cedd5cdcc5db39. Each artifact is checksummed as it is written, uploaded to this repository, and removed from the job workspace before the next artifact is produced.

skippy-model-package write-package "/hf-cache/NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-MTPv2-00001-of-00003.gguf" --out-dir "/tmp/meshllm-layer-job-meshllm_NVIDIA-Nemotron-3-Super-120B-A12B-UD-Q4_K_XL-MTPv2-layers-1/package"

Links

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

How it works

How language models work

Your prompttext / messagesTransformerattention over tokensNext-token loopgenerate + streamResponsetext · tool callsA language model reads your tokens and predicts the next one, again and again, streaming the reply back.
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