---

license: other

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

tags:

- gguf

- quantized

- apex

- moe

- mixture-of-experts

- nvidia

- nemotron

- mamba

- hybrid

- multimodal

- vision

- audio

- reasoning

---

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Nemotron-3-Nano-Omni-30B-A3B-Reasoning — APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16.

Brought to you by the LocalAI team | APEX Project | Technical Report

Available Files

| File | Profile | Size | Best For |

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

| Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-I-Balanced.gguf | I-Balanced | 26 GB | Best overall quality/size ratio |

| Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-Balanced.gguf | Balanced | 26 GB | General purpose |

| Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-I-Quality.gguf | I-Quality | 22 GB | Highest quality with imatrix |

| Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-Quality.gguf | Quality | 22 GB | Highest quality standard |

| Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-I-Compact.gguf | I-Compact | 19 GB | Consumer GPUs, best quality/size |

| Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-Compact.gguf | Compact | 19 GB | Consumer GPUs |

| Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-I-Mini.gguf | I-Mini | 18 GB | Smallest "safe" tier |

| Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-I-Nano.gguf | I-Nano | 17 GB | Experimental — IQ2_XXS mid-layer experts |

| Nemotron-3-Nano-Omni-30B-A3B-Reasoning-F16.gguf | F16 reference | 59 GB | Full-precision reference (text-only) |

| mmproj.gguf | Vision+audio projector | ~1.6 GB | Required for image and audio understanding |

What is APEX?

APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient — edge layers get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).

The key insight: in MoE models, expert FFN tensors make up the bulk of model weight but only 6/128 experts activate per token. APEX compresses middle-layer experts more aggressively while preserving edge layers (first/last 5) and keeping attention, SSM/Mamba, and shared expert tensors at higher precision.

See the APEX project for full details, technical report, and scripts.

Nano (experimental tier)

The APEX Nano tier pushes mid-layer routed experts to IQ2_XXS (2.06 bpw), near-edge to IQ2_S, edges to Q3_K, with shared experts kept at Q5_K. About 5% smaller than Mini with modest quality cost — viable only on MoE thanks to sparse per-token expert activation. Requires imatrix.

Benchmarks pending. Feedback welcome.

Multimodal Support

This is the Omni variant — supports text + vision + audio inputs. The included mmproj.gguf (sourced from unsloth) provides:

• Vision: RADIO ViT encoder (1280-dim)
• Audio: Parakeet encoder (1024-dim, 24 layers)

Pass --mmproj mmproj.gguf to llama.cpp / LocalAI to enable multimodal inference. Note: llama.cpp's audio output is not yet supported in mtmd — audio input only.

Architecture

• Outer model: NemotronH_Nano_Omni_Reasoning_V3 (multimodal wrapper)
• Inner LLM: NemotronH (NemotronHForCausalLM) — same as Nemotron-3-Nano-30B-A3B
• Layers: 52 (23 Mamba-2, 23 MoE, 6 attention) per pattern MEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEME
• Experts: 128 routed + 1 shared (6 active per token)
• Total Parameters: 30B (LLM only) + RADIO + Parakeet
• Active Parameters: ~3.5B per token
• Hidden size: 2688
• Context: 262,144 tokens
• APEX Config: 5+5 symmetric edge gradient across 52 layers
• Calibration: v1.3 diverse dataset (chat, code, reasoning, multilingual, tool-calling, Wikipedia)

Run with LocalAI

local-ai run mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF@Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-I-Balanced.gguf

Credits

• Base model: NVIDIA Nemotron team
• Vision+audio mmproj: unsloth
• APEX quantization: LocalAI team
• Built on llama.cpp (with PR #22481 — Nemotron Nano 3 Omni convert support)