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README.md

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+---
+license: other
+base_model: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
+tags:
+  - gguf
+  - quantized
+  - apex
+  - moe
+  - mixture-of-experts
+  - nvidia
+  - nemotron
+  - mamba
+  - hybrid
+---
+
+# Nemotron-3-Nano-30B-A3B APEX GGUF
+
+**APEX (Adaptive Precision for EXpert Models)** quantizations of [NVIDIA-Nemotron-3-Nano-30B-A3B](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16).
+
+**Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team** | [APEX Project](https://github.com/mudler/apex-quant) | [Technical Report](https://github.com/mudler/apex-quant/blob/main/paper/APEX_Technical_Report.pdf)
+
+## Benchmark Results
+
+Benchmarks coming soon. For reference APEX benchmarks on the Qwen3.5-35B-A3B architecture, see [mudler/Qwen3.5-35B-A3B-APEX-GGUF](https://huggingface.co/mudler/Qwen3.5-35B-A3B-APEX-GGUF).
+
+## 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).
+
+See the [APEX project](https://github.com/mudler/apex-quant) for full details, technical report, and scripts.
+
+## Architecture
+
+- **Model**: NVIDIA-Nemotron-3-Nano-30B-A3B (NemotronH)
+- **Layers**: 52 (23 Mamba-2, 23 MoE, 6 GQA attention)
+- **Experts**: 128 routed + 1 shared (6 active per token)
+- **Total Parameters**: 30B
+- **Active Parameters**: ~3.5B per token
+- **APEX Config**: 5+5 symmetric edge gradient across 52 layers
+
+## Run with LocalAI
+
+```bash
+local-ai run mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF@Nemotron-3-Nano-30B-A3B-APEX-I-Balanced.gguf
+```
+
+## Credits
+
+APEX is brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team. Developed through human-driven, AI-assisted research. Built on [llama.cpp](https://github.com/ggerganov/llama.cpp).