---
library_name: mlx
license: other
license_name: nvidia-nemotron-open-model-license
license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/
base_model: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
base_model_relation: quantized
language:
- en
- es
- fr
- de
- ja
- it
tags:
- mlx
- oQe
- enhanced
- mixed-precision
- moe
- nvidia
track_downloads: true
---

# NVIDIA-Nemotron-3-Nano-30B-A3B — oQe Series

This repository contains **Enhanced (oQe)** MLX quants for **NVIDIA-Nemotron-3-Nano-30B-A3B**. These builds utilize a high-precision anchoring strategy specifically tuned for NVIDIA's Mixture-of-Experts (MoE) architecture to prevent "expert collapse" and routing errors at lower bitrates.

### 🚀 The oQe (Enhanced) Build Path
MoE models like Nemotron-3 Nano are particularly sensitive to quantization because small errors in the gating logic can lead to the wrong expert being activated. Our build path includes:

1.  **Expert Sensitivity Analysis:** We identify which of the "Active-3" experts are most critical for specialized tasks (Code, Math, Reasoning) and protect their weights.
2.  **MoE Gating Protection:** The router and attention heads are locked to a minimum of 6-bit or 8-bit precision to ensure token routing remains accurate.
3.  **Hessian-Based Tuning:** Post-quantization adjustment is applied to the expert blocks to recover the "drift" caused by compressing the massive parameter space.

### 📋 oQ Build Performance Matrix

| Tier | Target bpw | Actual bpw | Size | Precision Boosts | Hybrid Plan / Strategy |
|---|---|---|---|---|---|
| [**oQ8e**](https://huggingface.co/splats/NVIDIA-Nemotron-3-Nano-30B-A3B-oQ8e) | 8.0 | **8.00** | 32.82 GB | 0 | Full 8-bit Static |
| [**oQ6e**](https://huggingface.co/splats/NVIDIA-Nemotron-3-Nano-30B-A3B-oQ6e) | 6.0 | **6.57** | 27.21 GB | 48 | 8bitX48 (Router & Head Anchors) |
| [**oQ5e**](https://huggingface.co/splats/NVIDIA-Nemotron-3-Nano-30B-A3B-oQ5e) | 5.0 | **5.64** | 23.36 GB | 118 | 8bitX48, 6bitX70 |
| [**oQ4e**](https://huggingface.co/splats/NVIDIA-Nemotron-3-Nano-30B-A3B-oQ4e) | 4.0 | **4.70** | 18.94 GB | 118 | 8bitX48, 6bitX48, 5bitX22 |

### 🛠 Technical Build Audit
* **Calibration:** Uses a 128-sample dataset ($128 \times 256$ tokens) covering the diverse language set (EN, ES, FR, DE, JA, IT).
* **Sensitivity Proxy:** Nano-30B-A3B-oQ8.
* **MoE Strategy:** Aggressive anchoring. We used 48 mandatory 8-bit anchors for the routing logic and early attention blocks to maintain the 30B parameter logic flow.

## Model Highlights
* **Active-3 MoE:** Only 3 billion parameters are active per token, providing the inference speed of a small model with the broad knowledge base of a 30B model.
* **Multilingual:** Maintains strong performance across the 6 primary languages supported by the base model.
* **Architecture:** Hybrid MoE structure optimized for efficiency on Apple Silicon.

---
**Acknowledgments:** These quants were built using the [oMLX](https://github.com/jundot/omlx) framework. The weight optimization process is based on the **GPTQ** algorithm by Frantar et al.

*Verified via Splats Lab Vault v2.8. These models are standard mlx-lm compatible and work with any app supporting MLX safetensors.*