---

license: other

license_name: nvidia-open-model-license

license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf

base_model: nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16

tags: [nemotron, multimodal, mamba2, moe, quantized, rotorquant, gguf, llama.cpp,

llama-mtmd, multimodal-via-mmproj]

library_name: gguf

pipeline_tag: image-text-to-text

language: [en]

datasets: [nvidia/Nemotron-Image-Training-v3]

inference: false

---

> [!TIP]

> KV-cache quantization without any fork (recommended, 2026): upstream

> llama.cpp/Ollama now cover this natively — use -ctk q8_0 -ctv q8_0

> (~half KV memory, negligible quality loss: perplexity +0.002–0.05) or

> -ctk q4_0 -ctv q4_0 (~quarter memory, ≈7.6% perplexity increase). In

> Ollama: OLLAMA_KV_CACHE_TYPE=q8_0 with OLLAMA_FLASH_ATTENTION=1. Keep

> K and V types symmetric to stay on the fast fused Flash-Attention path.

> Since April 2026, mainline llama.cpp also applies Hadamard rotation to

> KV activations (PR #21038),

> which greatly improves low-bit KV quality (opt-out:

> LLAMA_ATTN_ROT_DISABLE=1).

>

> The RotorQuant/TurboQuant fork flow below is experimental/legacy: the

> TurboQuant llama.cpp PR was closed without merging (June 2026) and the fork

> is unmaintained relative to mainline. It is NOT required to use this model.

<!-- kv-upstream-note -->

Nemotron-3-Nano-Omni-30B-A3B-Reasoning - RotorQuant GGUF Q5_K_M

GGUF Q5_K_M quantization of Nemotron-3-Nano-Omni-30B-A3B-Reasoning (nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16) with RotorQuant weight method.

The Q5_K_M.gguf binary in this repo is loaded by llama.cpp / llama-mtmd-cli.

For multimodal inference (text + image + audio + video) pair this with the

multimodal projector: majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16.

For the matched-KV stack — RotorQuant weights + RotorQuant KV-cache modifier —

For the runtime KV-cache modifier itself (weight-agnostic), see

majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant.

Quickstart

# 1. Download the GGUF + the multimodal projector
huggingface-cli download majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q5_K_M Q5_K_M.gguf --local-dir ./model
huggingface-cli download majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16 mmproj-F16.gguf --local-dir ./mmproj

# 2. Multimodal inference (text + image + audio + video)
llama-mtmd-cli \
  -m ./model/Q5_K_M.gguf \
  --mmproj ./mmproj/mmproj-F16.gguf \
  --image cat.jpg \
  -p "Describe this image in detail" \
  --temp 0.6 --top-p 0.95 -n 512

# 3. Text-only inference (no mmproj needed)
llama-cli \
  -m ./model/Q5_K_M.gguf \
  -p "What is the capital of France?" \
  --temp 0.6 --top-p 0.95 -n 256

# Disable extended reasoning (default is on):
#   add `--chat-template-kwargs '{"enable_thinking": false}'`

> ⚠️ Do NOT use llama.cpp built against CUDA 13.2 — produces gibberish. Pin CUDA 12.x or use Metal/CPU.

Modality matrix

| Modality | Encoder | Quantization in this variant |

|---|---|---|

| Text | LLM backbone (Mamba-2 + Transformer hybrid Sparse MoE) | per the variant suffix |

| Image | CRADIO v4-H | BF16 (kept full-precision in every non-GGUF variant; GGUF uses mmproj-F16 split file) |

| Audio | Parakeet-TDT-0.6B-v2 | BF16 (same rationale) |

| Video | Parakeet-TDT-0.6B-v2 + frame sampler | BF16 (≤ 2 min, 256 frames @ 2 FPS) |

NVIDIA's official FP8 / NVFP4 recipe keeps both encoders + the cross-modal

MLP projectors in BF16 to preserve multimodal accuracy. We follow that

convention in every quantized variant we ship.

Runtime quirks

llama.cpp

Use llama-mtmd-cli for multimodal inference; pass --mmproj mmproj-F16.gguf

(see majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16).

Do NOT use CUDA 13.2 — produces gibberish. Pin CUDA 12.x or

use the Metal/CPU paths.

Ollama

Text-only; multimodal is blocked because Ollama doesn't yet support

the mmproj split-file pattern.

Reasoning mode

enable_thinking defaults to True. To disable extended reasoning

(e.g., for latency-sensitive cases), pass enable_thinking=False

to the chat template / generate call. No separate "no-think"

variant card exists — this is a runtime flag, not a model variant.

Quant trade-off (GGUF lane)

| Quant | Approx size | Use case | Recommendation |

|---|---|---|---|

| Q2_K | ~17 GB | Lossy, low-RAM CPU/edge | Resource-constrained inference |

| Q3_K_M | ~19 GB | Smaller-than-Q4, modest quality drop | Edge devices with ~16 GB RAM |

| IQ4_XS | ~16 GB | Importance-quant 4-bit, smaller than Q4_K_M | Best size/quality at 4-bit |

| Q4_K_M | ~23 GB | Balanced default | Recommended for most users |

| Q5_K_M | ~24 GB | Higher fidelity than Q4 | Quality-sensitive applications |

| Q6_K | ~28 GB | Approaching FP16 quality | High-fidelity CPU/edge |

| Q8_0 | ~32 GB | Near-lossless reference | Fidelity-critical work |

| MXFP4_MOE | ~17 GB | Microscaling FP4 (MoE-aware) | vLLM / transformers users |

(Current variant — Q5_K_M — is bolded.)

Variants in this family

(Showing 56 sibling variants under majentik/nemotron3-nano-omni-30b-*. The current variant — RotorQuant-GGUF-Q5_K_M — is bolded.)

| Variant | Runtime | Approx size | Use case |

|---|---|---|---|

| RotorQuant-GGUF-Q5_K_M | llama.cpp | ~40 GB | Higher fidelity, more RAM |

About the RotorQuant / TurboQuant labels

RotorQuant and TurboQuant are this project's release labels, not distinct

quantization algorithms — for any given tier, both brand repos carry

byte-identical weights produced with the standard MLX / llama.cpp quantizers.

No brand-specific speedup is claimed or measured.