Nemotron 3 Nano Omni 30B A3B Reasoning TurboQuant GGUF MXFP4 MoE

[!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.

Nemotron-3-Nano-Omni-30B-A3B-Reasoning - TurboQuant GGUF MXFP4_MOE

GGUF MXFP4_MOE quantization of Nemotron-3-Nano-Omni-30B-A3B-Reasoning (nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16) with TurboQuant weight method.

The MXFP4_MOE.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 — TurboQuant weights + TurboQuant KV-cache modifier — see majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-MXFP4_MOE-TQ-KV. For the runtime KV-cache modifier itself (weight-agnostic), see majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant.

Quickstart

# 1. Download the GGUF + the multimodal projector
huggingface-cli download majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-MXFP4_MOE MXFP4_MOE.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/MXFP4_MOE.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/MXFP4_MOE.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

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)

(Current variant — MXFP4_MOE — is bolded.)

Variants in this family

(Showing 56 sibling variants under majentik/nemotron3-nano-omni-30b-*. The current variant — TurboQuant-GGUF-MXFP4_MOE — is bolded.)